]> ULISSY s.r.l.

Via Gaetano Sacchi 16 Roma RM 00153 Italy cayerbe@gmail.com

TU Dresden

Dresden Germany muhammad_usama.sardar@tu-dresden.de

The Capital Index

Austin, Texas USA nikolaichuk.s.f@gmail.com

Security RATS trajectory identity proof-of-humanity breadcrumb attestation RATS criticality This document specifies the Trajectory-based Recognition of Identity Proof (TRIP) protocol, a decentralized mechanism for establishing claims of physical-world presence through cryptographically signed, spatially quantized location attestations called "breadcrumbs." Breadcrumbs are chained into an append-only log, bundled into verifiable epochs, and distilled into a Trajectory Identity Token (TIT) that serves as a persistent pseudonymous identifier. The protocol employs a Criticality Engine, a statistical anomaly detector informed by complex-systems research, to distinguish biological movement from synthetic trajectories. Power Spectral Density (PSD) analysis of the displacement series yields a scaling exponent alpha that captures long-range temporal correlation in movement. A six-component Hamiltonian energy function scores each breadcrumb against the identity's learned behavioral profile in real time. This revision (-05) reframes the statistical core of the Criticality Engine in light of empirical validation against human-mobility data: the PSD scaling exponent alpha is specified as a per-deployment-calibrated scored feature rather than a fixed classification gate; the fixed [0.30, 0.80] "biological" band is withdrawn in favor of calibrated ranges; the asserted Levy-PSD bridge is replaced by a statement of the independence of the step-size and temporal-correlation features; and a new validation section reports separation results and methodology. The cryptographic chain, RATS mapping, Active Verification Protocol, and privacy model are unchanged from -04. TRIP is designed to be transport-agnostic and operates independently of any particular naming system, blockchain, or application layer.

Introduction Conventional approaches to proving that an online actor corresponds to a physical human being rely on biometric capture, government-issued documents, or knowledge-based challenges. Each technique introduces a centralized trust anchor, creates honeypots of personally identifiable information (PII), and is susceptible to replay or deepfake attacks. TRIP takes a fundamentally different approach: it treats sustained physical movement through the real world as evidence of embodied existence. A TRIP-enabled device periodically records its position as a "breadcrumb" -- a compact, privacy-preserving, cryptographically signed attestation that the holder of a specific Ed25519 key pair was present in a particular spatial cell at a particular time. An adversary who controls only digital infrastructure cannot fabricate a plausible trajectory because doing so requires controlling radio-frequency environments (GPS, Wi-Fi, cellular, IMU) at many geographic locations over extended periods. This document specifies the data structures, algorithms, and verification procedures that constitute the TRIP protocol. It intentionally omits transport bindings, naming-system integration, and blockchain anchoring, all of which are expected to be addressed in companion specifications.

Requirements Language The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 when, and only when, they appear in all capitals, as shown here.

Terminology Terms defined in the RATS Architecture (Attester, Evidence, Verifier, Attestation Result, Relying Party) are used throughout this document with their RFC 9334 meanings. Additional terms specific to TRIP:

Breadcrumb:

A single, signed attestation of spatiotemporal presence. The atomic unit of TRIP Evidence.

Trajectory:

An ordered, append-only chain of breadcrumbs produced by a single identity key pair.

Epoch:

A bundle of breadcrumbs (default 100) sealed with a Merkle root, forming a verifiable checkpoint.

Trajectory Identity Token (TIT):

A pseudonymous identifier derived from an Ed25519 public key paired with trajectory metadata.

Criticality Engine:

The analytical subsystem that evaluates trajectory statistics for signs of biological Self-Organized Criticality (SOC). In RATS terms, the Criticality Engine is a component of the Verifier.

PSD Scaling Exponent (alpha):

The slope of the Power Spectral Density of the displacement time series in log-log space. This quantity is referred to in the spectral analysis literature as the "spectral exponent" or "scaling exponent." In human mobility, alpha reflects long-range temporal correlation in movement and tends to be higher than for memoryless synthetic processes. TRIP uses alpha as an empirically-calibrated statistical feature (), not as a fixed physical constant; any specific numeric range is a per-deployment calibration. The 1/f "pink noise" framing, motivated by Parisi's work on scale-free correlations in biological systems , is the historical motivation for the approach, not a normative boundary.

Hamiltonian (H):

A weighted energy function that quantifies how much a new breadcrumb deviates from the identity's learned behavioral profile.

Anchor Cell:

An H3 cell where an identity has historically spent significant time (e.g., home, workplace).

Flock:

The set of co-located TRIP entities whose aggregate movement provides a reference signal for alignment verification.

Proof-of-Humanity (PoH) Certificate:

A compact Attestation Result containing only statistical exponents derived from the trajectory, with no raw location data.

RATS Architecture Mapping TRIP implements the Remote ATtestation procedureS (RATS) architecture defined in . This section provides the normative mapping between TRIP components and RATS roles, establishes the attestation topology, and frames the detailed protocol mechanics defined in subsequent sections.

Role Mapping RATS Role TRIP Component Description Attester TRIP-enabled mobile device Collects breadcrumbs, signs them with the identity Ed25519 private key, chains them into the append-only trajectory log, and transmits H3-quantized Evidence to the Verifier. Evidence Breadcrumbs and epoch records H3-quantized spatiotemporal claims including cell identifiers, timestamps, context digests, chain hashes, and Ed25519 signatures. Evidence is transmitted from Attester to Verifier. Verifier Criticality Engine Receives Evidence, performs chain verification, computes PSD scaling exponents (), fits Levy flight parameters (), evaluates the six-component Hamiltonian (), and produces Attestation Results. Attestation Result PoH Certificate and trust score Contains only statistical exponents (alpha, beta, kappa) and aggregate scores. No raw Evidence (cell IDs, timestamps, chain hashes) is included in the Attestation Result. See . Relying Party Any service consuming PoH Certificates Evaluates the Attestation Result against its own policy. Does not receive or process raw Evidence.

Attestation Topology TRIP's Active Verification Protocol () implements the RATS background-check model: the Relying Party initiates verification by sending the identity's public key and a freshness nonce to the Verifier, which challenges the Attester, evaluates the Evidence, and returns a PoH Certificate (Attestation Result) to the Relying Party.

Verifier | Verifier --[challenge]--> Attester Verifier <-[response]--- Attester | Relying Party <---[PoH Certificate]------- Verifier ]]>

The background-check model is the REQUIRED attestation topology for TRIP. It ensures that every Attestation Result is bound to a specific Relying Party challenge, preventing replay of certificates across contexts. A passport-model deployment, where the Attester obtains a PoH Certificate in advance and presents it directly to Relying Parties, is not prohibited but provides weaker freshness guarantees. In the passport model, the certificate's validity duration (field 11 of the PoH Certificate, ) and the chain head hash (field 13) provide the only freshness binding. Relying Parties accepting passport-model certificates SHOULD require short validity durations. TRIP proposes the use of post-handshake attestation via for integration with standard RATS attestation flows.

Evidence Flow H3-quantized Evidence is transmitted from the Attester to the Verifier. This is an explicit design choice: the Verifier requires access to the full breadcrumb chain to compute PSD scaling exponents, fit Levy flight parameters, and evaluate the Hamiltonian. Privacy preservation derives from the H3 quantization transform applied by the Attester before any data leaves the device, NOT from data locality. Raw GPS coordinates MUST NOT be transmitted. The quantization transform is lossy and irreversible. The Verifier MUST NOT forward raw Evidence to Relying Parties. Only the Attestation Result (PoH Certificate) is disclosed to Relying Parties.

Verifier Trust Model The Relying Party MUST trust the Verifier that produced the Attestation Result. The TRIP protocol supports multiple independent Verifiers. An Attester MAY submit Evidence to more than one Verifier. A Relying Party MAY accept Attestation Results from any Verifier it trusts. Each Verifier MUST have its own Ed25519 key pair. The Verifier signs PoH Certificates with its private key (field 14). Relying Parties verify this signature against the Verifier's published public key.

Breadcrumb Data Structure A breadcrumb is encoded as a CBOR map with the following fields: Key CBOR Type Description 0 uint Index (sequence number) 1 bstr (32) Identity public key (Ed25519) 2 uint Timestamp (Unix seconds) 3 uint H3 cell index 4 uint H3 resolution (7-10) 5 bstr (32) Context digest (SHA-256) 6 bstr (32) / null Previous block hash 7 map Meta flags 8 bstr (64) Ed25519 signature

Spatial Quantization The H3 geospatial indexing system partitions the Earth's surface into hexagonal cells at multiple resolutions. TRIP employs resolutions 7 through 10: Resolution Avg. Area Edge Length Use Case 7 ~5.16 km^2 ~1.22 km Rural / low-density 8 ~0.74 km^2 ~0.46 km Suburban / general 9 ~0.11 km^2 ~0.17 km Urban / high-density 10 ~0.015 km^2 ~0.07 km Default / standard verification A conforming implementation MUST quantize raw GPS coordinates to an H3 cell before any signing or storage operation. Raw coordinates MUST NOT appear in breadcrumbs or in any protocol message transmitted between TRIP entities. H3 resolution is a configurable protocol parameter. Implementations SHOULD default to resolution 10. Deployments MAY select alternative resolutions based on jurisdictional requirements, population density, and use-case sensitivity. Lower resolutions (larger cells) provide stronger location privacy at the cost of reduced spatial discrimination for trust computation.

Context Digest Computation The context digest binds ambient environmental signals to the breadcrumb without revealing them. The digest is computed as follows: Construct a pipe-delimited string of tagged components in the following order: "h3:" followed by the H3 cell hex string "ts:" followed by the timestamp bucketed to 5-minute intervals (floor(Unix_minutes / 5) * 5) "wifi:" followed by the first 16 hex characters of SHA-256(sorted comma-joined BSSIDs), if Wi-Fi scan data is available "cell:" followed by the first 16 hex characters of SHA-256(sorted comma-joined tower IDs), if cellular data is available "imu:" followed by the first 16 hex characters of SHA-256(IMU vector string), if inertial sensor data is available Compute SHA-256 over the UTF-8 encoding of the resulting string. Absent components MUST be omitted entirely, not represented as empty strings.

Signature Production The signable payload is the deterministic CBOR encoding (per Section 4.2 of ) of a CBOR map containing fields 0 through 7, with map keys sorted in ascending integer order. The Ed25519 signature is computed over the raw bytes of this CBOR encoding and stored at key 8.

Deterministic CBOR encoding ensures that any conforming implementation produces identical byte sequences for the same logical content, which is essential for reproducible signature verification across heterogeneous platforms.

Block Hash and Chaining The block hash is the SHA-256 digest of the complete deterministic CBOR encoding of the breadcrumb (fields 0 through 8 inclusive, i.e., including the signature):

Each breadcrumb at index > 0 MUST carry the block hash of its immediate predecessor in field 6, forming an append-only hash chain. The genesis breadcrumb (index 0) MUST set field 6 to null (CBOR simple value 22).

Chain Management

Location Deduplication Proof-of-Trajectory requires demonstrated movement. A conforming implementation MUST reject a breadcrumb if the H3 cell is identical to the immediately preceding breadcrumb. Implementations SHOULD also enforce a cap (default 10) on the number of breadcrumbs recordable at any single H3 cell to prevent stationary farming.

Minimum Collection Interval Breadcrumbs SHOULD be collected at intervals of no less than 15 minutes. An implementation MAY allow shorter intervals during explicit "exploration" sessions but MUST NOT accept intervals shorter than 5 minutes.

Chain Verification A Verifier MUST check: Index values form a contiguous sequence starting at 0. Timestamps are monotonically non-decreasing. Each previousHash matches the block hash of the prior breadcrumb. Each Ed25519 signature verifies against the identity public key and the canonical signed data.

Epochs An epoch seals a batch of breadcrumbs (default 100) under a Merkle root. The epoch record is a CBOR map containing: Key Type Description 0 uint Epoch number 1 bstr (32) Identity public key 2 uint First breadcrumb index 3 uint Last breadcrumb index 4 uint Timestamp of first breadcrumb 5 uint Timestamp of last breadcrumb 6 bstr (32) Merkle root of breadcrumb hashes 7 uint Count of unique H3 cells 8 bstr (64) Ed25519 signature over fields 0-7 The Merkle tree MUST use SHA-256 and a canonical left-right ordering of breadcrumb block hashes. An epoch is sealed when the breadcrumb count reaches the epoch size threshold.

Trajectory Identity Token (TIT) A TIT is the externally presentable identity derived from a TRIP trajectory. It consists of: The Ed25519 public key (32 bytes). The current epoch count. The total breadcrumb count. The count of unique H3 cells visited. A trust score (see ). A TIT SHOULD be encoded as a CBOR map for machine consumption and MAY additionally be represented as a Base64url string for URI embedding.

The Criticality Engine The Criticality Engine is the core analytical component of the TRIP Verifier. It evaluates whether a trajectory exhibits the statistical signature of biological Self-Organized Criticality (SOC) -- the phenomenon where living systems operate at the boundary between order and chaos, producing scale-free correlations that are mathematically distinct from synthetic or automated movement. The theoretical foundation rests on three pillars: First, Parisi's demonstration that flocking organisms such as starling murmurations exhibit scale-free correlations where perturbations propagate across the entire group regardless of size. Crucially, Ballerini et al. showed that these interactions are topological (based on nearest k neighbors) rather than metric (based on distance) . TRIP draws on this through Power Spectral Density analysis (): human movement exhibits long-range temporal correlations that memoryless and step-size-matched synthetic generators do not reproduce. quantifies this separation against real human-mobility data and an informed adversary; the claim rests on that validation rather than on Self-Organized Criticality as an established property of individual trajectories. Second, Barabasi et al.'s discovery that human displacement follows truncated Levy flights with approximately 93% predictability . TRIP learns each identity's mobility profile -- displacement distribution, anchor transition patterns, and circadian rhythms -- and detects deviations from these learned baselines (). Third, a six-component Hamiltonian energy function () that combines spatial, temporal, kinetic, flock-alignment, contextual, and structural analysis into a single anomaly score for each incoming breadcrumb. The Hamiltonian provides real-time detection while the PSD and mobility statistics provide aggregate trajectory assessment.

Power Spectral Density Analysis The primary diagnostic is the Power Spectral Density (PSD) of the displacement time series. Given a trajectory of N breadcrumbs with displacements d(i) between consecutive breadcrumbs, the PSD is computed via the Discrete Fourier Transform:

The PSD is then fitted to a power-law model:

The exponent alpha is the PSD scaling exponent -- referred to in the spectral analysis literature as the "spectral exponent" or "scaling exponent." In the context of human mobility, this quantity captures the degree of long-range temporal correlation in movement patterns. The theoretical significance of this exponent derives from Parisi's work demonstrating that biological systems operating at criticality produce characteristic scale-free correlations . The PSD scaling exponent is the critical diagnostic: Alpha (reference calibration) Indicative interpretation (score input, not a gate) <= 0.16 Memoryless / synthetic-like (low human-likeness) 0.16 - 1.25 Consistent with human temporal correlation >= 1.25 Drift / deterministic (low human-likeness) The bounds in are the reference calibration derived in ; they are indicative score inputs, not a classification gate, and MUST be re-derived per deployment. A conforming implementation MUST compute the PSD scaling exponent over a sliding window of the most recent 64 breadcrumbs (minimum) to 256 breadcrumbs (recommended). The alpha estimator is the log-periodogram (ordinary least-squares) regression of log-PSD on log-frequency -- the Geweke and Porter-Hudak estimator . A conforming implementation MUST record the window length N and the fitted frequency range, and MUST reuse the identical window length and frequency range when calibrating and when scoring: alpha values obtained under different window lengths or fitted frequency ranges are not comparable. The estimator's standard error, under log-periodogram asymptotics, is approximately

where F is the set of fitted frequency bins and ell_bar is the mean of the log-frequencies (not the log of the mean frequency). This is a model-dependent approximation under log-periodogram assumptions, not a universal guarantee for finite, quantized, deduplicated, or irregularly sampled trajectories; numeric SE values and any sample-length threshold derived from them MUST be verified for the deployment's pipeline before use as a conformance criterion. The PSD scaling exponent is a scored feature, not a binary classification gate. It contributes to the Criticality Confidence () and, through it, to trust scoring (). An implementation MUST NOT accept or reject a trajectory on the basis of alpha alone. Empirical validation () shows that a fixed narrow band such as [0.30, 0.80] rejects the majority of genuine human trajectories; such a band MUST NOT be used as a hard acceptance criterion. Deployments MUST calibrate the acceptance range and the alpha score against representative human-mobility data for their population, processed through the identical pipeline: the same H3 resolution (), deduplication rule (), collection interval (), window length, and fitted frequency range. A calibration is not transferable to a deployment that differs in any element of this tuple. The key insight is that automated movement generators tend to lack the long-range temporal correlations ("memory") present in human movement. A random walk produces white noise (alpha near 0). A deterministic replay produces brown noise (alpha near 2). Human trajectories fall between these extremes, but not in a band as narrow as earlier revisions assumed; the calibrated range and the achievable separation are established empirically in . NOTE: Earlier revisions specified a fixed "biological" band [0.30, 0.80] centered at 0.55, informed by literature on 1/f-like properties of human motion . Direct validation against a human-mobility dataset () found that this fixed band does not hold for individual trajectories processed through the TRIP pipeline: it excludes most genuine human trajectories. The alpha acceptance range is therefore a per-deployment calibrated parameter, and alpha is used as a score rather than a gate.

Criticality Confidence Score The Criticality Confidence is a value in [0, 1] derived from the PSD scaling exponent relative to the deployment's calibrated human range:

When fit_reliability is 1.0 (the default), criticality_confidence reduces to alpha_score: a single-factor score. A deployment using the default MUST describe the result as a one-factor alpha score and MUST NOT present it as the combination of two independent reliability factors. The coefficient of determination (R-squared) of the log-log power-law regression MUST NOT be used as the fit_reliability term. The periodogram is chi-squared distributed at each frequency, so R-squared of a log-log line is not a valid goodness-of-fit measure; empirically it is near zero () even for genuine human trajectories and would collapse the confidence to zero. The achievable R-squared is in fact limited by the estimator's own noise. Under the stated log-periodogram assumptions, the approximate expected explained-variance fraction is

so at human-range alpha this expected fraction is small -- on the order of the observed median R-squared near 0.04. It is an approximation of the expected value, not a guaranteed maximum across realizations, and it is a property of the estimator rather than of the trajectory; it is model-dependent and MUST be verified for the deployment's frequency selection before being quoted. fit_reliability SHOULD instead be derived from the calibrated separability of the alpha score (), or set to 1.0 where no better estimator is available. A criticality_confidence below 0.5 SHOULD trigger elevated monitoring. A value below 0.3 SHOULD flag the trajectory for manual review or additional verification challenges.

The Levy Exponent Does Not Determine the Spectral Exponent Earlier revisions posited an analytical "bridge" in which the truncated-Levy displacement exponent beta () predicts the PSD scaling exponent alpha () via alpha ~ (3 - beta) * g, placing alpha in the assumed biological range. Empirical validation () does not support this relationship: synthetic trajectories drawn from the truncated-Levy law of with beta ~ 1.75 -- matching the human step-size distribution -- produce alpha values near zero, far outside any human-calibrated range. The correct statement is narrower: the marginal distribution of step magnitudes does not, by itself, determine their temporal correlation structure. beta characterizes the distribution of step magnitudes; alpha characterizes the temporal correlation structure of the displacement series. For independent, identically distributed step magnitudes the magnitude series is spectrally white regardless of the marginal step-size distribution, so matching the human step-size distribution does not reproduce the human temporal-correlation signature. This is not a claim of universal statistical independence of alpha and beta in real human trajectories, where the two may be coupled by the underlying mobility process. This separation is a security property, not a defect. An adversary who replicates the displacement law -- a documented, learnable distribution -- does not thereby reproduce the alpha signature; in validation () such an adversary remains separable from genuine human trajectories by alpha. The property TRIP relies on is temporal correlation rather than the step-size distribution; the evidence establishes discrimination against the tested adversaries, not that temporal correlation is intrinsically difficult for a sophisticated adversary to reproduce. Implementations MUST NOT infer the PSD scaling exponent from the Levy displacement exponent using the previously proposed analytical relationship, and MUST NOT use their agreement as an internal-consistency check.

Convergence Analysis The PSD scaling exponent, Levy flight parameters, and flock alignment metrics are fundamentally ensemble properties derived from statistical physics. Applying them to a single trajectory raises the question: how many breadcrumbs are required for these ensemble properties to converge on an individual trajectory with a given confidence level? This section provides guidance on convergence behavior. Definitive false positive and false negative rates require empirical validation against real-world datasets (e.g., GeoLife, MDC), which is planned for a companion publication. The framework below describes the expected convergence properties and the protocol's mitigation strategies.

Convergence Regimes The reliability of TRIP's statistical classifiers depends on trajectory length. Three regimes are identified: Breadcrumbs Regime PSD Reliability Levy Fit Reliability 0 - 63 Bootstrap Not computed (insufficient data for DFT) Not reliable 64 - 199 Provisional Computed but with wide confidence intervals; SE(alpha) large relative to the calibrated band (illustrative) Beta estimated but kappa poorly constrained 200+ Stable SE(alpha) small relative to the calibrated band (illustrative); R-squared stays at its estimator ceiling () and is not used as a fit measure Both beta and kappa well-constrained The trust scoring formula () incorporates profile maturity through the factor min(breadcrumb_count / 200, 1.0), which scales Hamiltonian weights during the bootstrap and provisional regimes.

Composition of Independent Tests TRIP does not rely on any single statistical test. The six-component Hamiltonian () combines independent classifiers: spatial Levy fit, temporal Markov properties, kinetic transition analysis, flock alignment, IMU cross-correlation, and chain structural integrity. Even if each individual test has a significant error rate on a short trajectory, the composition of independent tests reduces the combined error probability. The components are not statistically independent (spatial and kinetic both derive from displacement), so a closed-form product bound over per-test error rates is not meaningful and is not claimed here. The value of the composition is defense-in-depth: an adversary must defeat multiple partially-independent features simultaneously. Combined false-positive and false-negative rates are an empirical question addressed in .

Error Cost Asymmetry TRIP's classification errors have asymmetric costs: False negative (human classified as bot): Low cost. The identity accumulates more breadcrumbs and is reclassified correctly as the trajectory lengthens. No permanent damage occurs. False positive (bot classified as human): Higher cost, but requires simultaneous spoofing across all six Hamiltonian components -- spatial displacement statistics, temporal circadian patterns, Markov transition probabilities, flock alignment, IMU cross-correlation, and chain timing regularity. This represents a significantly harder adversarial problem than defeating any single test.

Minimum Breadcrumbs for Classification Based on the convergence analysis above, the minimum trajectory lengths for classification decisions are: 64 breadcrumbs: Minimum for PSD computation. Sufficient for preliminary screening (reject obvious bots) but not for positive human classification. 100 breadcrumbs: Minimum for handle claiming (). The Levy fit becomes usable and the Markov transition matrix begins to stabilize. 200 breadcrumbs: RECOMMENDED for reliable positive human classification. At this length, the PSD alpha estimate has variance below 0.05 and the Levy parameters are well-constrained. 256+ breadcrumbs: Sufficient for high-confidence classification suitable for high-stakes Relying Party decisions. The standard error of informs the usable window length: the window SHOULD be long enough that SE(alpha) is small relative to the calibrated half-width alpha_halfwidth, and an acceptance band narrower than the estimator's own spread will reject genuine trajectories. A precise conformance test for "sufficiently long" is left to per-deployment validation and is not fixed by this document. For the reference calibration, figures from the estimator variance alone indicate window lengths on the order of N >= 62 for a usable estimate and N >= 204 for the reference band. These numbers are illustrative: they MUST be re-derived from the estimator and validated for the deployment's pipeline before use as conformance thresholds. Determining precise false positive and false negative rates at each breadcrumb count requires empirical validation. Implementers SHOULD reproduce the validation of and test against representative human-mobility datasets to establish ROC curves and confidence intervals for their specific deployment parameters.

Empirical Validation The statistical claims of this section were validated against real human mobility data and synthetic adversaries. Human trajectories were drawn from the GeoLife GPS dataset and processed through the TRIP pipeline exactly as specified (H3 resolution 10 quantization per , deduplication per , and the collection interval of ). Synthetic adversaries -- random walk, Gaussian walk, correlated random walk, truncated-Levy (the law), scripted deterministic movement, and integrated drift -- were generated and processed identically. The exponent alpha was computed by the method and, independently, by Detrended Fluctuation Analysis (DFA) as a non-stationarity-robust cross-check. Principal findings: As a score, alpha separates human from synthetic trajectories with area under the ROC curve approximately 0.85 against pooled adversaries and approximately 0.78 against an informed truncated-Levy adversary using the law. A fixed band [0.30, 0.80] classifies only about one third of genuine human trajectories (Section method) as biological -- unsuitable as a hard acceptance gate. A reference two-sided calibration of approximately [0.16, 1.25] yields human acceptance of roughly 60-68% at synthetic false-positive rates of 5-8%. A two-sided range is required: an integrated-drift adversary reaches alpha near 1.8. The log-log power-law fit is poor (median R-squared near 0.04), which is why R-squared is not used as a confidence measure (). the marginal step-size distribution does not determine the temporal-correlation structure (): an adversary matching the human step-size law does not reproduce the human alpha signature. These results derive from a specific dataset and adversary set and are bounded accordingly; they establish the calibration methodology and the qualitative separation, not universal error rates. The methodology and artifacts are available for reproduction and per-deployment recalibration . The following structural results are independent of any particular dataset and were reported from numerical simulation by S. Nikolaichuk; they are stated here pending independent reproduction. First, for independent, identically distributed step magnitudes the step-magnitude spectrum is white, so the estimator responds to temporal correlation and not to the marginal step-size law. Second, the log-periodogram R-squared is bounded by the estimator's own noise (), so a low R-squared is expected and is not evidence of a poor trajectory. Third, three pipeline parameters -- H3 resolution, the deduplication rule, and the window length -- can bias the estimated alpha, with downward effects reported under the tested simulation conditions; the observed direction depends on those conditions, which is why calibration MUST hold the full pipeline tuple () fixed. These are properties of the estimator and the pipeline, not measurements of the GeoLife sample.

Mobility Statistics This section defines the mobility model that enforces known constraints of human movement, as established by Barabasi et al. .

Truncated Levy Flights Human displacement between consecutive recorded locations follows a truncated power-law distribution:

The exponent beta captures the heavy-tailed nature of human movement: most displacements are short (home to office) but occasional long jumps (travel) follow a predictable distribution. The cutoff kappa is learned per identity and represents the characteristic maximum range. A conforming implementation MUST maintain a running estimate of beta and kappa for each identity by fitting the displacement histogram using maximum likelihood estimation over the most recent epoch (100 breadcrumbs). A new displacement that falls outside the 99.9th percentile of the fitted distribution MUST increment the spatial anomaly counter. As established in , beta (step-size distribution) and alpha (temporal correlation) are independent features. Implementations MUST NOT treat agreement between the fitted beta and the observed alpha as a consistency check; they measure different properties and are evaluated separately.

Trajectory Predictability Research has demonstrated that approximately 93% of human movement is predictable based on historical patterns . TRIP exploits this by maintaining a Markov Transition Matrix over anchor cells:

An anchor cell is defined as any H3 cell where the identity has recorded 5 or more breadcrumbs. The transition matrix is rebuilt at each epoch boundary. The predictability score Pi for an identity is the fraction of observed transitions that match the highest-probability successor in the Markov matrix. Human identities converge toward Pi values in the range [0.80, 0.95] after approximately 200 breadcrumbs. Deviations below 0.60 are anomalous.

Circadian and Weekly Profiles The implementation SHOULD maintain two histogram profiles: A circadian profile C[hour] recording the probability of activity in each hour of the day (24 bins). A weekly profile W[day] recording the probability of activity on each day of the week (7 bins). These profiles provide the temporal baseline for the Hamiltonian temporal energy component ().

The Six-Component Hamiltonian To assess each incoming breadcrumb, the Criticality Engine computes a weighted energy score H that quantifies how much the breadcrumb deviates from the identity's learned behavioral profile. High energy indicates anomalous behavior; low energy indicates normalcy.

Component Weight Diagnostic Target H_spatial 0.25 Displacement anomalies (teleportation) H_temporal 0.20 Circadian rhythm violations H_kinetic 0.20 Anchor transition improbability H_flock 0.15 Misalignment with local human flow H_contextual 0.10 Sensor cross-correlation failure H_structure 0.10 Chain integrity and timing regularity Weights are modulated by the profile maturity m, defined as min(breadcrumb_count / 200, 1.0). During the bootstrap phase (m < 1.0), all weights are scaled by m, widening the acceptance threshold for new identities.

H_spatial: Displacement Anomaly Given the identity's fitted truncated Levy distribution P(delta_r), the spatial energy for a displacement delta_r is the negative log-likelihood (surprise):

Typical displacements yield H_spatial near the identity's historical baseline. A displacement that exceeds the identity's learned kappa cutoff by more than a factor of 3 produces an H_spatial value in the CRITICAL range.

H_temporal: Rhythm Anomaly

Activity at 3:00 AM for an identity with a 9-to-5 circadian profile yields high H_temporal. Activity at 8:00 AM on a Tuesday for the same identity yields low H_temporal.

H_kinetic: Transition Anomaly

A home-to-office transition at 8:00 AM yields low H_kinetic. An office-to-unknown-city transition yields high H_kinetic.

H_flock: Topological Alignment Inspired by Parisi's finding that starlings track their k nearest topological neighbors (k approximately 6-7) rather than all birds within a metric radius , the flock energy measures alignment between the identity's velocity vector and the aggregate velocity of co-located TRIP entities.

When flock data is unavailable (sparse network or privacy constraints), the implementation SHOULD fall back to comparing the current velocity against the identity's own historical velocity distribution at the same location and time-of-day. H_flock defeats GPS replay attacks: an adversary replaying a previously recorded trajectory will find that the ambient flock has changed since the recording, producing a misalignment signal.

H_contextual: Sensor Cross-Correlation

Implementations that lack IMU access MUST set H_contextual = 0 and SHOULD increase the weights of other components proportionally.

H_structure: Chain Structural Integrity Inter-breadcrumb timing regularity: excessively uniform intervals suggest automation. Hash chain continuity: any break in the chain produces maximum H_structure. Phase-space smoothness: the velocity-acceleration phase portrait of a human trajectory traces smooth loops, while bots produce either chaotic blobs or tight limit cycles.

Alert Classification The total Hamiltonian H maps to an alert level. The baseline H_baseline is the rolling median of the identity's own recent energy values, making the threshold self-calibrating per identity: H Range Level Action [0, H_baseline * 1.5) NOMINAL Normal operation [H_baseline * 1.5, 3.0) ELEVATED Increase sampling frequency, log [3.0, 5.0) SUSPICIOUS Flag for review, require reconfirmation [5.0, infinity) CRITICAL Freeze trust score, trigger challenge

Proof-of-Humanity Certificate A PoH Certificate is a compact, privacy-preserving Attestation Result (in the RATS sense) asserting that an identity has demonstrated biological movement characteristics. It contains ONLY statistical exponents derived from the trajectory -- no raw location data, no GPS coordinates, no cell identifiers. The certificate is encoded as a CBOR map: Key Type Description 0 bstr (32) Identity public key 1 uint Issuance timestamp 2 uint Epoch count at issuance 3 float PSD scaling exponent alpha (informational: audit and recalibration input; not gated on) 4 float Levy beta exponent 5 float Levy kappa cutoff (km) 6 float Predictability score Pi 7 float Criticality confidence 8 float Trust score T 9 uint Unique cell count 10 uint Total breadcrumb count 11 uint Validity duration (seconds) 12 bstr (16) Relying Party nonce (REQUIRED) 13 bstr (32) Chain head hash at issuance (REQUIRED) 14 bstr (64) Verifier Ed25519 signature 15 map Calibration reference (); REQUIRED when field 7 is populated Fields 12 and 13 are REQUIRED in all PoH Certificates. Field 15 is REQUIRED whenever field 7 (criticality confidence) is populated (). Every certificate MUST be issued in response to an Active Verification request (). There is no passive issuance mode. A Relying Party receiving a PoH Certificate can verify: The Verifier signature (field 14) is valid against a trusted Verifier public key. The criticality confidence (field 7) -- which incorporates the calibrated alpha score () -- meets the Relying Party's policy threshold. Relying Parties MUST NOT gate on a fixed alpha band; see . The trust score (field 8) meets application requirements. The certificate has not expired (field 1 + field 11 > current time). The nonce (field 12) matches the Relying Party's original challenge. The chain head hash (field 13) provides freshness binding. The calibration reference (field 15), when field 7 is used: if an inline profile is present, its deterministic-CBOR SHA-256 digest matches the referenced digest; and the issuing Verifier and calibration profile are acceptable under the Relying Party's policy (). The certificate reveals NOTHING about where the identity has been -- only that it has moved through the world in a manner statistically consistent with a biological organism.

Calibration Provenance The criticality confidence (field 7) is computed relative to a deployment's calibrated human range () and is therefore meaningful only together with the calibration that produced it. Field 15 carries that calibration, bound into the signed certificate, so a Relying Party can interpret field 7 and decide whether two Verifiers' values are comparable. Field 15 is a calibration reference: a SHA-256 digest of an immutable, versioned calibration profile, with an OPTIONAL inline copy of the profile.

bstr .size 32, ; profile-digest ? 1 => calibration-profile ; OPTIONAL inline copy } calibration-profile = { 0 => uint, ; profile_version 1 => tstr, ; estimator_id 2 => uint, ; h3_resolution 3 => tstr, ; deduplication_rule 4 => uint, ; collection_interval_s 5 => uint, ; window_n 6 => [float, float], ; fitted_frequency_range [lo, hi] 7 => float, ; alpha_center 8 => float, ; alpha_halfwidth 9 => tstr, ; reliability_model 10 => tstr, ; calibration_dataset_id 11 => uint ; calibration_date } ]]>

The profile is encoded in the core deterministic CBOR encoding of (Section 4.2.1). The profile digest is the SHA-256 of that deterministic encoding:

When an inline profile (key 1) is present, a recipient MUST compute its digest and MUST reject the certificate if the computed digest does not equal the referenced digest (key 0).

Signature Coverage The Verifier signature (field 14) MUST be computed over a domain-separated deterministic encoding of the certificate with field 14 removed:

where "TRIP-PoH-Certificate-v1" is the ASCII domain-separation label and cert-without-14 is the certificate map containing every field except field 14, encoded per Section 4.2.1. Field 15 is thereby covered by the signature. Verification recomputes signing-input and checks the signature. CBOR map key order does not affect coverage: the signed bytes are the deterministic encoding, not the certificate as serialized on the wire. Reproducible test vectors (profile digest, Verifier public key, and signature for a worked example using an all-zero test seed) are published with the validation artifacts (). For the example profile above, the profile digest is 9a0d67a2df0752b121acd94ee53955b0386fc39049fef43919e7de254e901c1e.

Comparing Scores Across Verifiers Two field-7 values are comparable only if their profile digests are equal, or the Relying Party has an explicit, validated policy for interpreting one profile in terms of the other. Identical profile digests identify identical declared calibration semantics; they do NOT establish equivalent evidence quality or Verifier trustworthiness. A Relying Party MUST evaluate the issuing Verifier, the calibration profile, and the applicable authorization policy separately. A certificate without field 15 is a pre-provenance certificate. Its field 7 MUST NOT be treated as comparable across Verifiers, and MUST be accepted only under an explicit Relying Party policy.

Trust Scoring

The threshold for claiming a handle (binding a human-readable name to a TIT) requires breadcrumb_count >= 100 and T >= 20. A trajectory whose criticality confidence () falls below the deployment's calibrated threshold MUST have its trust score capped at 50, regardless of other factors.

Replay Protection and Active Verification TRIP provides replay protection at two distinct layers: protection of the Evidence chain against tampering, and protection of Attestation Results against replay to Relying Parties. All Attestation Results MUST be issued via the Active Verification Protocol described in this section.

Chain-Level Replay Protection The monotonically increasing index and the chaining via the previous block hash field provide replay protection within a single trajectory. A replayed breadcrumb will fail the chain integrity check. Cross-trajectory replay will fail Ed25519 signature verification.

Active Verification Protocol The Active Verification Protocol provides cryptographic freshness guarantees by binding the Attestation Result to a Relying Party-supplied nonce, the current chain head, and the current time. This is the ONLY verification mode supported by TRIP. There is no passive verification mode. The protocol proceeds as follows: The Relying Party generates an unpredictable nonce (RECOMMENDED: 16 bytes from a cryptographically secure random number generator) and sends a Verification Request to the Verifier:

bstr .size 32, ; identity public key 1 => bstr .size 16, ; nonce 2 => uint, ; request timestamp 3 => uint, ; requested freshness window (seconds) } ]]>

The Verifier delivers a Liveness Challenge to the Attester via a real-time channel (e.g., WebSocket push, push notification):

bstr .size 16, ; nonce (from Relying Party) 1 => bstr .size 32, ; verifier identity (public key) 2 => uint, ; challenge timestamp 3 => uint, ; response deadline (seconds) } ]]>

The Attester constructs and signs a Liveness Response binding the nonce to the current chain state:

bstr .size 16, ; nonce echo 1 => bstr .size 32, ; chain_head_hash 2 => uint, ; response timestamp 3 => uint, ; current breadcrumb index 4 => bstr .size 64, ; Ed25519 signature over fields 0-3 } ]]>

The Verifier validates the Liveness Response by checking: The Ed25519 signature (field 4) is valid against the identity's public key over fields 0-3. The nonce echo (field 0) matches the original Verification Request. The chain_head_hash (field 1) is consistent with the Verifier's stored trajectory state. The response timestamp (field 2) is within the deadline. The breadcrumb index (field 3) matches or exceeds the Verifier's last known index. Upon successful validation, the Verifier produces a fresh PoH Certificate with field 12 set to the nonce and field 13 set to the chain_head_hash, signs it, and returns it to the Relying Party. The Relying Party verifies the PoH Certificate per , confirming that field 12 matches its original nonce. If the Attester does not respond within the deadline, the Verifier MUST return an error. The Verifier MUST NOT issue a PoH Certificate without a valid Liveness Response.

Active Verification CDDL The following CDDL schema defines the Active Verification messages:

bstr .size 32, ; identity_key 1 => bstr .size 16, ; nonce 2 => uint, ; request_timestamp 3 => uint, ; freshness_window_seconds } liveness-challenge = { 0 => bstr .size 16, ; nonce 1 => bstr .size 32, ; verifier_key 2 => uint, ; challenge_timestamp 3 => uint, ; response_deadline_seconds } liveness-response = { 0 => bstr .size 16, ; nonce_echo 1 => bstr .size 32, ; chain_head_hash 2 => uint, ; response_timestamp 3 => uint, ; current_breadcrumb_index 4 => bstr .size 64, ; ed25519_signature } ]]>

Security Considerations

GPS Replay Attacks An adversary records a legitimate trajectory and replays the GPS coordinates on a different device. TRIP detects this through multiple channels: H_flock: the ambient flock has changed since the recording. H_contextual: unless the adversary also replays Wi-Fi BSSIDs, cellular tower IDs, and IMU data, the context digest will not match. H_structure: the timing regularity of a replay is typically either too perfect or shifted in a detectable pattern.

Synthetic Walk Generators PSD scaling exponent test: random walk generators produce white noise (alpha approximately 0) and drift generators produce high alpha. In validation () both fall well outside the calibrated human range and remain separable from human trajectories as a score; see for the measured separation. Levy flight fitting: synthetic displacements rarely match the truncated power-law distribution with biologically plausible beta and kappa values. Predictability test: synthetic trajectories either show near-zero predictability (random) or near-perfect predictability (scripted), both outside the human [0.80, 0.95] range.

Emulator Injection An adversary runs the TRIP client on an Android/iOS emulator with spoofed GPS. Detection relies on H_contextual (emulators lack real IMU data) and context digest (emulators lack real Wi-Fi and cellular data).

Device Strapping (Robot Dog Attack) An adversary straps a phone to a mobile robot or drone. This is the most sophisticated attack because it produces real GPS, Wi-Fi, cellular, and IMU data from actual physical movement. Mitigation relies on PSD analysis (robotic movement lacks 1/f noise), phase-space smoothness (H_structure), and circadian profiles. This attack remains an active area of research.

Verifier Compromise A compromised Verifier could issue fraudulent PoH Certificates. Mitigations: Relying Parties SHOULD accept certificates from multiple independent Verifiers; key rotation and revocation procedures SHOULD be established; the Active Verification Protocol ensures even a compromised Verifier cannot produce a valid certificate without the Attester's cooperation.

Denial of Service Verifiers SHOULD rate-limit requests per identity and per Relying Party. The Active Verification Protocol's real-time requirement provides an inherent rate limit on valid completions.

Statistical Classifier Limitations The Criticality Engine applies ensemble statistical properties (PSD scaling exponent, Levy flight parameters, flock alignment) to individual trajectories. As discussed in , the reliability of these classifiers depends on trajectory length. Implementers MUST be aware that: Classification confidence is low during the bootstrap regime (fewer than 64 breadcrumbs) and moderate during the provisional regime (64-199 breadcrumbs). alpha is a per-deployment-calibrated scored feature, not a fixed classification gate. Validation () found that a fixed [0.30, 0.80] band rejects the majority of genuine human trajectories; deployments MUST calibrate the range against population-specific data. The composition of six Hamiltonian components provides defense in depth, but the components are not independent and the combined error rate is an empirical quantity (), not a product of per-test rates. The separation results in derive from a specific dataset (GeoLife) and adversary set. They establish the calibration methodology and qualitative separation, not universal error rates; per-deployment validation against representative data remains REQUIRED. An acceptance band narrower than the estimator's standard error () will reject genuine trajectories, and the pipeline biases (), which reduced estimated alpha under the tested conditions, fall hardest on low-mobility users, whose displacement series carry less spectral information. Deployments SHOULD ensure the calibrated band is wider than SE(alpha) and SHOULD assess false-rejection impact on low-mobility populations. This draft makes no quantified fairness claim absent representative testing. Score shopping: an identity able to request many certificates may retry until a favorable criticality score is issued. Verifiers SHOULD bound the number of certificate requests per identity over a time window. Per-identity rate limiting alone does not address an adversary controlling many identities (Sybil); see and the Active Verification requirement ().

Privacy Considerations

Quantization-Based Privacy TRIP's privacy model is based on lossy spatial quantization, not on data locality. H3-quantized Evidence is transmitted from the Attester to the Verifier. Raw GPS coordinates MUST NOT be transmitted. At the default resolution 10, each cell covers approximately 15,000 m^2, providing meaningful ambiguity in populated areas.

Verifier Data Handling The Verifier MUST NOT forward raw Evidence to Relying Parties. The Verifier MUST disclose its data retention policy. The Verifier SHOULD retain only statistical aggregates and MAY discard individual breadcrumbs after incorporation. The Verifier MUST support data deletion where required by law.

Relying Party Data Minimization The PoH Certificate reveals statistical exponents and aggregate counts. A Relying Party does NOT learn which cities or specific locations the identity has visited, the identity's home or workplace, the identity's daily schedule, or any raw trajectory data.

Trajectory Correlation and Sybil Resistance A single physical entity operating multiple TRIP identities simultaneously constitutes a Sybil attack. TRIP raises the cost: each identity requires a separate physical device, weeks of sustained movement, and independent trajectory accumulation. The H_flock component provides detection of co-located trajectories with identical displacement vectors.

Population Density Considerations In sparsely populated areas, even cell-level granularity may narrow identification. Implementations SHOULD use lower resolution in rural areas and MAY allow users to override to a lower resolution at any time.

Deployment Considerations

Multiple Verifier Deployments Any entity that implements the verification procedures defined in this specification MAY operate as a TRIP Verifier. An Attester MAY submit Evidence to more than one Verifier.

Verifier Interoperability All conforming Verifiers MUST implement chain integrity verification, PSD scaling exponent classification, and the PoH Certificate format. Two Verifiers processing the same Evidence SHOULD produce consistent alpha, beta, and kappa values within numerical precision bounds.

Transport Binding This specification does not mandate a specific transport. Implementations MAY use HTTPS, WebSocket, CoAP, or any transport providing confidentiality and integrity protection. The Active Verification Protocol requires a real-time channel.

Naming System Integration The binding of human-readable names to TRIP identities is outside the scope of this specification and is expected to be addressed in a companion document.

Accessibility and Low-Mobility Users TRIP does not require geographic travel. It requires sustained physical existence over time. A person who remains in a single H3 cell generates a valid trajectory; the trust scoring formula assigns 20% weight to temporal continuity and 40% to breadcrumb count, both accumulating regardless of spatial diversity. The context digest provides environmental diversity even without movement. The Hamiltonian is self-calibrating per identity. For stationary users, implementations SHOULD supplement spatial PSD with temporal PSD (analyzing inter-breadcrumb timing patterns). Deployments MUST NOT impose minimum spatial diversity requirements that would exclude users with mobility limitations.

IANA Considerations This document has no IANA actions at this time. Future revisions may request CBOR tag assignments and a media type registration for application/trip+cbor.

This document describes elliptic curve signature scheme Edwards-curve Digital Signature Algorithm (EdDSA). The algorithm is instantiated with recommended parameters for the edwards25519 and edwards448 curves. An example implementation and test vectors are provided. This document proposes a notational convention to express Concise Binary Object Representation (CBOR) data structures (RFC 7049). Its main goal is to provide an easy and unambiguous way to express structures for protocol messages and data formats that use CBOR or JSON. The Concise Binary Object Representation (CBOR) is a data format whose design goals include the possibility of extremely small code size, fairly small message size, and extensibility without the need for version negotiation. These design goals make it different from earlier binary serializations such as ASN.1 and MessagePack. This document obsoletes RFC 7049, providing editorial improvements, new details, and errata fixes while keeping full compatibility with the interchange format of RFC 7049. It does not create a new version of the format. In network protocol exchanges, it is often useful for one end of a communication to know whether the other end is in an intended operating state. This document provides an architectural overview of the entities involved that make such tests possible through the process of generating, conveying, and evaluating evidentiary Claims. It provides a model that is neutral toward processor architectures, the content of Claims, and protocols. In many standards track documents several words are used to signify the requirements in the specification. These words are often capitalized. This document defines these words as they should be interpreted in IETF documents. This document specifies an Internet Best Current Practices for the Internet Community, and requests discussion and suggestions for improvements. RFC 2119 specifies common key words that may be used in protocol specifications. This document aims to reduce the ambiguity by clarifying that only UPPERCASE usage of the key words have the defined special meanings. An Entity Attestation Token (EAT) provides an attested claims set that describes the state and characteristics of an entity, a device such as a smartphone, an Internet of Things (IoT) device, network equipment, or such. This claims set is used by a relying party, server, or service to determine the type and degree of trust placed in the entity. An EAT is either a CBOR Web Token (CWT) or a JSON Web Token (JWT) with attestation-oriented claims. The Entity Attestation Token (EAT) is an extensible attestation version of a CBOR Web Token (CWT). EAT defines a location claim, but does not define a proximate location claim. This document proposes a claim in which an attester can relay detected relative location of a target. JPMorgan Chase & Co. Intel Telefonica Oracle Aryaka Fraunhofer SIT Operators of regulated, sovereign, and high-assurance deployments require hardware-rooted, machine-verifiable proof that a workload executes in its approved environment. Current remote attestation mechanisms address two relevant properties in isolation: platform integrity — that the hardware and software stack are in an approved, untampered state — and physical residency — that the hardware resides within an approved geographic boundary. Neither property alone is sufficient: integrity without residency permits a valid platform to operate outside approved boundaries; residency without integrity permits a compromised platform to claim valid placement. This document defines the *Verifiable Proof of Environment Attestation Profile (V-PEA)*, a profile of the RATS Architecture {{!RFC9334}} that fuses both properties into a single TPM-sealed Evidence structure (lah-bundle). V-PEA defines two Evidence dimensions: *WHAT* — hardware provenance (TPM Attestation Key registered and manufacturer-endorsed), platform integrity (firmware and OS state matching reference values), and workload agent software integrity (binary digest matching an approved value); and *WHERE* — physical residency within an approved geographic boundary. A TPM quote seal binds WHAT and WHERE into a single unforgeable statement: neither dimension can be forged or transplanted without invalidating the other. For the WHERE dimension, V-PEA supports Transparent Zero-Knowledge Proofs (ZKPs), enabling an Attester to prove geographic compliance without disclosing precise coordinates. A positive V-PEA Attestation Result enables a Relying Party to issue hardware-rooted credentials or authorize operations — combining verified execution environment (WHAT) with verified physical placement (WHERE) — before releasing sensitive assets or granting access. Integration with workload identity systems is described in the WIMSE Integration appendix. Sandelman Software Works Many workloads have limitations on what geography they are allowed to operate in. This is often due to a regulation that requires that the computation occur in a particular jurisdiction. There are many mechanisms by which Evidence of location may be created and then evaluated by a Verifier. No matter which mechanism is appropriate for a given situation, the result of the Verification can be expressed in a similiarly defined EAT Attestation Result. This document is about encoding a variety of geographical conclusions conclusions in an Attestation Result. In addition, one mechanism of directly creating a geographic result in the form of an Endorsement is described in an appendix. Uber Technologies The Nobel Foundation TU Dresden Linaro Nokia Intuit University of the Bundeswehr Munich Arm Limited This specification defines a method for two parties in a communication interaction to exchange Evidence and Attestation Results using exported authenticators, as defined in [RFC9261]. Additionally, it introduces the cmw_attestation extension, which allows attestation credentials to be included directly in the Certificate message sent during the Exported Authenticator-based post-handshake authentication. The approach supports both the passport and background check models from the RATS architecture while ensuring that attestation remains bound to the underlying communication channel.

Relationship to Other Geo-Location Proposals Several Internet-Drafts proposed in the RATS and WIMSE working groups address aspects of location attestation. This appendix positions TRIP relative to four such proposals.

EAT Location Claim The Entity Attestation Token defines a location claim that conveys a single point-in-time geographic position in WGS-84 coordinates. The claim provides no temporal depth, no privacy quantization, and no behavioral context. TRIP could emit its Trajectory Identity Token or Proof-of-Humanity Certificate as an EAT claim, providing longitudinal behavioral evidence within an EAT-formatted Attestation Result. The two are complementary.

Proximate Location Claim The Proximate Location Claim defines evidence of relative position derived from secure ranging between two devices, typically using ultra-wideband (UWB) radio per FiRa Consortium specifications. Proximate Location is instantaneous and pairwise; TRIP is longitudinal and self-attested. Proximate Location requires dedicated radio hardware in both endpoints; TRIP uses commodity GNSS and ambient signals already present on consumer mobile devices.

Verifiable Geofencing for Workloads The Verifiable Geofencing draft targets confidential computing workloads, providing cryptographically verifiable proof-of-residency that binds a workload identity to a host platform and a geographic boundary. Verifiable Geofencing answers "where is this workload executing?". TRIP answers "who is the human or autonomous entity behind this identity, and how has that entity moved over time?". The two address orthogonal concerns: residency versus operator integrity.

Geographic Attestation Results The Geographic Results draft defines how geographic location is conveyed within the RATS architecture as an attestation result, carrying the basis on which the location claim rests (for example, evidence, endorsement, or attestation result). It answers where an entity is, with explicit provenance for that assertion. TRIP instead conveys how an entity has moved over time as behavioral evidence of humanity; a TRIP token can be expressed as, or carried alongside, a geographic result. The two are complementary: location with provenance versus longitudinal operator integrity.

Comparison Summary Proposal Temporal Model Evidence Source Primary Question EAT Location Instantaneous Self-reported coordinate Where is this device right now? Proximate Location Instantaneous Secure ranging hardware Is device X near device Y? Verifiable Geofence Continuous (workload lifetime) TPM + sensor attestation Is this workload inside the boundary? Geographic Results Point-in-time RATS result (basis: evidence / endorsement / ...) Where is this entity, and on what basis? TRIP Longitudinal Self-attested behavioral trajectory Has this identity moved like a biological entity over time? TRIP is the only proposal in this set that uses behavioral trajectory over time as its source of evidence. The others provide instantaneous location claims, jurisdictional residency proofs, or one-time endorsements. TRIP is intended to coexist with these specifications, supplying a distinct dimension of evidence -- continuity of physical existence -- that none of them addresses.

History Version -01 introduced a Criticality Engine grounded in Giorgio Parisi's Nobel Prize-winning work on scale-free correlations and Albert-Laszlo Barabasi's research on the fundamental limits of human mobility . Version -02 formalized the mapping to the RATS Architecture , introduced the Active Verification Protocol with cryptographic freshness guarantees, and corrected the privacy model. This revision (-03) addresses three substantive issues identified through expert review by researchers working in the statistical physics of complex systems: First, it replaces the informal term "Parisi Factor" with the standard spectral analysis term "PSD scaling exponent alpha," properly attributing the theoretical foundation to Parisi's work without conflating tribute with established nomenclature. Second, it provides the missing analytical and numerical bridge between the Levy flight displacement exponent beta () and the PSD scaling exponent alpha (). Previous revisions asserted both as independent properties of human mobility without demonstrating their mathematical relationship. Third, it introduces a convergence analysis framework () that addresses the fundamental question of applying ensemble statistical properties to single trajectories, including guidance on minimum trajectory length and error rate estimation. Additionally, this revision removes Passive Verification mode entirely. All Attestation Results MUST now be bound to Relying Party nonces via the Active Verification Protocol (), eliminating the replay vulnerability identified in -02 review.

Changes from -02 This section summarizes the substantive changes from draft-ayerbe-trip-protocol-02: Replaced the term "Parisi Factor" with the standard spectral analysis term "PSD scaling exponent alpha" throughout the document. The theoretical contribution of Parisi's work is acknowledged in the motivation and terminology, not in the variable naming. Added (Levy-PSD Bridge) providing the analytical relationship between the Levy flight displacement exponent beta and the PSD scaling exponent alpha, with supporting references to empirical studies . Added (Convergence Analysis) addressing the application of ensemble statistical properties to single trajectories, including minimum trajectory length guidance and error rate estimation framework. Removed Passive Verification mode entirely (). All Attestation Results MUST now be produced via the Active Verification Protocol with Relying Party-supplied nonces. The PoH Certificate fields for nonce (field 12) and chain head hash (field 13) are now REQUIRED, not optional. Updated the PoH Certificate () to reflect mandatory Active Verification fields. Added references to recent empirical studies on spectral properties of human GPS trajectories.

Changes from -03 This section summarizes the changes from draft-ayerbe-trip-protocol-03: Added (Relationship to Other Geo-Location Proposals) positioning TRIP relative to four parallel proposals: EAT Location Claim, Proximate Location, Verifiable Geofencing, and PoP Endorsement, per the commitment made on rats@ietf.org in February. Added RFC 8610 reference and citation in . Editorial cleanup of .

Acknowledgements The TRIP protocol builds upon foundational work in cryptographic identity systems, geospatial indexing, statistical physics, and network science. The author thanks the contributors to the H3 geospatial system, the Ed25519 specification authors, and the broader IETF community for establishing the standards that TRIP builds upon. The Criticality Engine framework is inspired by the work of Giorgio Parisi on scale-free correlations in biological systems and Albert-Laszlo Barabasi on the fundamental limits of human mobility. The authors thank Jun Zhang for raising critical questions about accessibility and the applicability of mobility models to users with limited physical mobility, leading to the Accessibility and Low-Mobility Users section. The authors thank an anonymous reviewer from the statistical physics community for identifying three critical issues addressed in this revision: the need for standard spectral analysis terminology, the missing analytical bridge between Levy flight parameters and PSD scaling exponents, and the fundamental question of applying ensemble properties to single trajectories. These contributions shaped Sections 7.3 and 7.4 and the Section 13.7 discussion of statistical classifier limitations.