Biological Hash Generation With Domain Separation

Mechanism

The hash-generation pipeline operates as a sequence of well-defined stages, each of which contributes a distinct property to the final output. The first stage is biological signal acquisition, which captures a raw measurement of the physiological feature being used as the identity substrate, such as a fingerprint ridge pattern, an iris texture map, a hand-vein pattern, a heart-rate variability time series, or a multi-modal fusion of several such measurements. The raw measurement is high-entropy but non-canonical: two acquisitions of the same individual produce raw measurements that differ in framing, alignment, illumination, and instantaneous physiological state.

The second stage is noise-tolerant feature normalization. The raw measurement is reduced to a stable feature vector by a procedure designed to be invariant under the categories of variation that legitimately occur within the same individual across acquisitions while remaining sensitive to the categories of variation that distinguish individuals. The procedure typically combines geometric registration to a canonical pose, photometric normalization to a canonical illumination, and feature extraction by an attested feature-extraction function whose code identity is itself bound into the eventual hash. The stable feature vector is the first artifact in the pipeline whose value is approximately reproducible across acquisitions of the same individual.

The third stage is stable sketching. The stable feature vector is reduced to a binary sketch by a fuzzy extractor or equivalent locality-sensitive reduction that produces a fixed-width binary string with the property that small Hamming distances in the sketch correspond to small distances in feature space. The sketching uses helper data, a public auxiliary string that allows the same sketch to be reproduced from slightly different feature vectors of the same individual without itself revealing information about the feature vector. The helper data can be stored alongside the hash and disclosed without compromising privacy.

The fourth stage is domain-separated derivation. The stable sketch is combined with a context-specific domain separator and passed through a one-way derivation function to produce the final fixed-width hash. The domain separator binds the hash to the context in which it was produced, such that the same individual processed in two distinct contexts produces statistically independent hash outputs. The derivation function is a cryptographic primitive whose forward direction is computationally cheap but whose inverse is computationally infeasible; the hash output therefore carries no recoverable information about either the stable sketch, the feature vector, or the underlying biological signal.

The locality-sensitive property of the hash family is preserved across the derivation only with respect to within-context comparisons. Two hashes produced under the same domain separator from feature vectors of the same individual exhibit small Hamming distance; two hashes produced under different domain separators do not. This asymmetry is the source of the privacy guarantee: continuity is verifiable within a context, correlation is infeasible across contexts.

Operating Parameters

The hash output width is configurable and is selected based on the intended security level and the false-acceptance tolerance of the downstream verifier. Typical widths range from 128 bits for low-security applications to 256 or 512 bits for high-assurance applications. Output width interacts with the locality-sensitive distance threshold: larger widths provide finer continuity discrimination at the cost of more storage and longer transmission.

The locality-sensitivity radius parameter controls how much physiological drift the hash family will tolerate as a within-individual continuity match. Larger radii increase tolerance to legitimate physiological variation but decrease discrimination from physiologically similar but non-identical individuals. The radius is calibrated empirically against a population sample representing the deployment's expected variation distribution.

The helper data size is determined by the difference between the raw entropy of the feature vector and the desired entropy of the stable sketch. Larger helper data supports stronger noise tolerance but consumes more storage. The helper data is signed by the issuing authority's attestation key so that a compromised acquisition system cannot substitute helper data designed to weaken the eventual hash.

The domain separator is a per-context value drawn from a registry maintained by the operating authority for each individual or each operational deployment. The separator is structured as a tuple containing the context identifier, the deployment epoch, and a freshness nonce that prevents long-term cross-context correlation even if the context identifier alone is observed. Rotation of the domain separator across epochs forces re-enrollment under the new separator and breaks any latent correlation across the rotation boundary.

The derivation function family is parameterized by computational cost. High-cost derivation, using memory-hard or iterated primitives, raises the cost of brute-force forgery attempts in which an adversary searches the feature-vector space for a vector that hashes to a target value. The cost parameter is calibrated against the expected adversary's resources and is signed into the hash artifact so that a verifier can detect downgrading.

Alternative Embodiments

In a fingerprint-based embodiment, the raw measurement is a ridge-and-minutia map captured by a contact or contactless fingerprint sensor; the stable feature vector is a canonical minutia-graph embedding; the stable sketch is a fuzzy extractor over the minutia-graph; and the domain-separated hash is suitable for use as a workplace, healthcare, or financial credential. In an iris-based embodiment, the raw measurement is a near-infrared iris texture image; the stable feature vector is a Gabor-filter response stack; the stable sketch is a code-based reduction; and the hash is suitable for use in high-security authentication where the false-acceptance tolerance must be vanishingly small.

In a cardiovascular-signal embodiment, the raw measurement is a continuous heart-rate-variability or photoplethysmographic time series captured by a wearable device; the stable feature vector is a spectral-energy embedding over a defined window; the stable sketch is a quantile-based reduction; and the hash supports continuous-presence attestation where the verifier must continuously confirm that a previously-enrolled individual remains the same individual across the wearable's session. In a multi-modal-fusion embodiment, several biological signals are acquired in parallel, their feature vectors are concatenated under attested weighting, and the stable sketch is computed jointly across the fused vector, producing a hash whose forgery resistance benefits from the difficulty of jointly forging multiple physiological substrates.

In a delegated-derivation embodiment, the stable sketch is computed within an attested enclave on the acquisition device, transmitted to the operating authority under attestation, and the domain-separated derivation is performed by the authority. The embodiment supports use cases where the acquisition device cannot be trusted with the domain separator. In a self-derivation embodiment, the entire pipeline executes within an attested device under the individual's control, and only the final hash is transmitted, supporting use cases where the individual must retain custody of the biological signal.

Composition With Other Cognition Primitives

The biological hash composes with the credentialed-observation mesh: the hash is itself a credentialed observation, signed by the producing authority and admitted by downstream consumers under their own policy. A consumer that admits the producing authority can verify continuity from one acquisition to the next; a consumer that does not admit the producing authority cannot use the hash at all and cannot extract any cross-context correlation from it.

The biological hash composes with the operator-intent envelope: an envelope can require a fresh biological hash as a precondition of admission, ensuring that the operating individual at envelope admission is the same individual previously enrolled in the operating unit's identity registry. The verification-feedback loop can additionally require periodic re-acquisition of the hash during long-window operation to detect substitution attacks in which the original individual is replaced mid-cycle.

The biological hash composes with the cross-context unlinkability primitive: explicit linking authorizations issued by the individual can pair domain-separated hashes across two contexts under a one-time link token, enabling consensual cross-context identity transfer without enabling general correlation. The link token is itself a credentialed object that expires according to the individual's specification.

Prior-Art Distinction

Conventional biometric template storage stores either the raw biometric, an encrypted copy, or a non-locality-sensitive hash. Raw and encrypted storage permits reconstruction by anyone with the storage and the encryption key. Non-locality-sensitive hashes do not tolerate physiological variation and produce false rejections under legitimate drift. Conventional cancelable biometric schemes apply per-user transformations but do not provide cross-context unlinkability under a domain separator and do not couple to a credentialed-observation mesh.

Conventional fuzzy extractors and secure-sketch constructions produce locality-sensitive sketches but do not include domain separation and do not produce hashes whose authority-binding is verifiable by downstream consumers without trusting the producing system. Conventional hashed-biometric authentication systems use a single global hash that becomes a universal correlator across deployments. Conventional zero-knowledge-proof biometric systems can produce unlinkable proofs but do not produce a fixed-width hash artifact suitable for storage, indexing, and continuity comparison in operational identity registries.

The biological hash disclosed herein is distinguished by the conjunction of locality-sensitive hash family, attested feature-extraction binding, fuzzy-extractor stable sketching, per-context domain-separated derivation, authority-signed helper data, and credentialed propagation through the surrounding observation mesh. Each element appears in some prior reference; the conjunction does not.

Disclosure Scope

The disclosure covers the biological hash as an architectural primitive applicable to any individual identity substrate that can be reduced to a stable feature vector under a noise-tolerant normalization. The primitive is independent of the specific biological modality, the specific feature-extraction function, the specific stable-sketch construction, and the specific derivation primitive, provided that the four-stage pipeline structure and the locality-sensitive, domain-separated, authority-bound output properties are preserved.

The disclosure expressly contemplates extension to revocable biological hashes in which compromise of a hash triggers domain-separator rotation and re-enrollment without re-acquisition; to threshold biological hashes in which the hash is split across multiple domain separators and reconstruction requires a quorum; to time-bounded biological hashes in which the hash carries a validity window beyond which the verifier must demand re-acquisition; and to delegated biological hashes in which the individual authorizes a custodian to produce hashes on the individual's behalf under a custody envelope. The scope of the disclosure is the architectural primitive and its claim language captures the conjunction of locality-sensitive family, stable-sketch reduction, domain-separated derivation, authority-signed helper data, and credentialed mesh propagation as the inventive contribution.