Privacy-preserving real-time system for validating and mitigating threats to structured healthcare transactions

Abstract

Computer-implemented system for real-time validation and threat mitigation for structured health transactions, including: a client policy repository that contains client-specific validation rules, consent restrictions, and risk thresholds; a trusted policy execution enclave that only allows policies and models after remote attestation of code and configuration, and decrypts and processes protected fields exclusively within the enclave; a grammatically aware streaming validator that enforces format and semantic constraints and synthesizes an auto-repair suggestion in case of parser errors, transforming inputs into a policy-compliant form with minimal changes; a hybrid risk engine that calculates rule-based and learned risk scores and furthermore evaluates a subset of features using data protection-preserving calculations to determine risk contributions without decoding selected fields; a causal graph generator that correlates related transactions over time into an entity-resolved, time-aligned graph and identifies impossibility patterns with edge-wise responsibility values; a coordinator for federated learning who updates models from client-local aggregates under differential privacy budgets and suspends aggregation when a distribution drift above a policy threshold is detected; an audit subsystem that generates zero-knowledge evidence demonstrating that selected compliance predicates for a transaction have been met without disclosing protected health information; a risk-adaptive flow control system that directs high-risk substreams into micro-quarantine sections with enhanced testing while maintaining the basic service quality for low-risk substreams; and a register that stores a validity context mark which links each decision to the identity of the policies, models, scope of consent and runtime environment used.

Description

AREA OF INVENTION

[0001] The invention relates to computer-implemented systems that validate, evaluate, and control incoming health transactions in real time, while maintaining confidentiality and verifiability. It relates to data protection-compliant calculation, secure execution, anomaly and fraud detection, and verifiable compliance for structured electronic health data exchanged between payers, providers, clearinghouses, and health platforms. BACKGROUND OF THE INVENTION

[0002] The healthcare revenue cycle and clinical workflows rely on structured transactions (e.g., eligibility checks, approvals, claims processing, payment advices). Conventional validators and rule sets are typically batch-oriented, rely on trust assumptions in the execution environment, or are tightly focused on syntax and can (i) miss semantic inconsistencies across related datasets; (ii) expose sensitive fields to operators or third-party processors; (iii) fail to provide cryptographic proof that policy and consent restrictions have been met; (iv) be subject to machine learning model drift, causing unnoticed performance regressions; and (v) over-block sources in response to anomalies, causing operational disruptions.Existing audit trails often only prove that "something was executed," but not that the correct policies and models were executed at a specific time for a specific client under a demonstrably secure configuration. Therefore, there is a need for an integrated, low-latency system that (a) validates both form and meaning, (b) enforces execution in a remotely attested secure environment, (c) supports privacy-preserving risk calculation and federated learning, (d) generates cryptographic evidence of compliant behavior without exposing protected data, and (e) mitigates threats through fine-grained flow control rather than blanket, source-wide blocking. SUMMARY OF THE INVENTION

[0003] In one aspect, a computer-implemented system performs privacy-preserving, real-time validation and threat mitigation for structured healthcare transactions. The system includes: a tenant policy repository; a remotely attested, trusted policy execution enclave; a grammar-aware streaming validator with semantic constraints and auto-repair suggestions; a hybrid risk engine that combines rule features with learned detectors and encryptedly evaluates selected features; a causal graph generator that correlates related transactions over time; a federated learning coordinator with budgets for differential privacy and a drift guard; an audit layer that generates concise, zero-knowledge evidence that predefined compliance predicates have been met; and a risk-adaptive flow control that directs only high-risk substreams into micro-quarantine lanes.a deception subsystem that embeds non-clinical beacons for exfiltration detection; a model integrity checker with rule-only failsafe; and a tamper-proof registry that stores a validity context marker linking each decision to the policies, models, consent scopes, and environmental conditions actually used. DEFINITIONS

[0004] "Structured health transaction" refers to a data set that conforms to an industry-standard health messaging or data exchange format. "Validity context mark" refers to a content-addressed fingerprint that encodes the active policy bundle, model identifiers, consent scope, and runtime / environment versions in effect at decision time. "Remote attestation" refers to verification by a relying party that code and configuration in a secure domain meet expected metrics. "Zero-knowledge proof" refers to cryptographic evidence that establishes the truth of a statement without disclosing the underlying data. "Micro-quarantine path" refers to an isolated processing path that applies stricter budgets and audits only to the high-risk portion of the data stream. DETAILED DESCRIPTION OF THE INVENTION

[0005] The system processes client-related healthcare transactions via authenticated channels in a secure, low-latency pipeline. A policy repository maintains specifications for each client (validation rules, consent and purpose restrictions, geographic / data residence guidelines, and thresholds for risk mitigation measures). An orchestrator initiates a policy execution enclave and loads signed policies and models only after remote attestation is complete. All decision logic—syntactic / semantic validation, risk assessment, and release control—is executed within the enclave, unless explicitly designated as a privacy-preserving calculation performed outside the enclave using encrypted methods.Each processed transaction provides: (i) a decision (accept, repair, hold, quarantine or reject), (ii) human-readable justifications with attribute attributions, (iii) optional auto-repair suggestions, (iv) a validity context mark and (v) audit artifacts including zero-knowledge evidence for selected predicates (e.g., "Consent scope includes action", "Combination of modifier and code is valid", "Amount thresholds met").

[0006] Trusted Policy Execution Enclave with Remote Attestation: A secure execution domain (hardware-backed or hypervisor-isolated) hosts the validator, risk module, and policy interpreter. Decryption keys for protected fields are bound to the measured state of the enclave. New or modified policies / models are accepted only after successful remote attestation of the enclave identity and manifest hashes. Runtime telemetry exported from the enclave is minimized and privacy-compliant. If attestation fails or integrity checks are triggered, the system reverts to a minimal, rule-based mode or terminates processing according to the policy. Grammar-aware streaming validator with automatic repair, streaming parser and validation

[0007] A streaming parser validates both structural and cross-field constraints. The validator supports: Context-free grammar rules for format conformity; Semantic constraints across segments (e.g., compatibility of diagnostic procedure, date window, vendor qualifiers); External lookup operations, where permitted (e.g., reference tables); latency-limited operation on substreams; and An auto-repair synthesizer suggests minimal corrections (insert, delete, replace) to transform erroneous input into policy-compliant forms while preserving business semantics. Repair suggestions are flagged, logged, and optionally subjected to a user approval workflow.

[0008] Hybrid Risk Engine with Privacy-Compliant Feature Analysis: The risk engine combines (a) deterministic features (frequency limits, magnitude deviations, cross-client uniqueness signals) and (b) learned signals (sequence models, outlier detectors), trained on client or cohort data. For a compact set of highly relevant features, the engine supports encrypted analysis using partially homomorphic operations or secure multi-party computation, enabling the determination of risk contributions without decrypting these sensitive elements. Feature attributions and calibrated uncertainty intervals are provided for operator transparency. Causal graph builder and motif detectors

[0009] Related transactions (same person / provider / facility / time slot) are fused into a time-aligned, entity-resolved graph. Edges carry provenance information as well as weights for marginal latency and risk contributions. Motive detectors identify impossibility patterns (e.g., procedure before diagnosis, mutually exclusive codes, unrealistic pathways between facilities) and output edge-by-edge responsibility scores that can trigger hold or micro-quarantine routing. Federated learning with data privacy budgets and drift guards

[0010] For cross-client model improvement, a coordinator orchestrates federated rounds in which only model updates or privacy-preserving aggregates cross client boundaries. Budgets for differential data protection (ε, δ) are tracked per client and per feature family. A drift monitor tracks distribution shifts and suspends aggregation if correlated anomalies or adversarial drift are detected. Model artifacts are signed; version lineage is logged in the registry. Zero-Knowledge Audit Evidence

[0011] When policies require verifiable compliance without data disclosure, the system generates concise cryptographic evidence that certain predicates have been met (e.g., "authorised provider performed coded procedure," "consent purpose includes requested use," "amount below risk threshold"), referencing public parameters and a verification key. Evidence objects and verification references are stored in the registry without any protected health information. Risk-adaptive flow control and micro-quarantine sections

[0012] Instead of blocking entire sources, the data plane controller calculates a routing intent per key (e.g., entity, facility, code class) and redirects only high-risk substreams to micro-quarantine paths with tightened budgets, additional checks, synthetic traffic canaries, and, if necessary, human approval review. Low-risk traffic remains at normal quality of service. Actions are reversible and logged with validity context markers. Deceptive beacons for active defense

[0013] To quickly detect exfiltration or misuse, the system can embed client-specific, non-clinical canary fields into a regulated portion of the data records (never used for clinical decisions). The occurrence of a beacon outside authorized pathways triggers immediate containment (key revocation, enclave reset, route quarantine) and generates a separate incident record. Model integrity and fail-safe

[0014] Before inference, model artifacts are verified against the signatures defined in the policy manifests. Lightweight adversarial guards (input consistency checks, influence-based alerts) protect against manipulated input. In case of an alert, the system switches to a pure rule mode and notifies the operators. Register and Scene of Validity

[0015] Each decision record contains the validity context mark: a cryptographic digest of the policy bundle, model identifiers, consent scope, and hashes of the environment state. The register stores the mark, attestation evidence, optional zero-knowledge evidence, and action results, thereby providing reproducible, client-specific change provenance. Best way of implementing the invention

[0016] One production version uses: a secure execution domain with measured boot and remote attestation; keys to decrypt certain fields are tied to the attested measurement; a streaming validator with a compiled grammar and semantic rule set; subrecords are validated within a budget of 50-150 ms; the auto-repair feature searches for minimal tree edit transformations and marks their trustworthiness; a risk engine with calibrated detectors; the encrypted evaluation is limited to half a dozen scalar features (e.g., moving count, sum, maximum) to maintain latency; A causal graph, built over a sliding window of 7-30 days with entity resolution through probabilistic assignment under strict consent; motive queries run in sub-seconds via pre-indexed constraints; federated learning cycles on a daily basis; client-specific budgets for differential data protection in accordance with policy; a drift monitor tracks distribution shifts using statistical distances; Zero-knowledge proofs for a small set of predicates; proof objects and verifier references are stored in the register; Flow shaping through programmable data-plane filters; micro-quarantine sections enforce stricter parallelism, rates, and additional predicate checks, and inject synthetic canary load to validate the section; Deception beacons, derived from a client secret and rotated; detection outside the allowed limit triggers immediate containment; a validity context marker, calculated as a hash over (Digest of the policy bundle || Model IDs || Descriptor of the scope of consent || Digest of the environment state) and anchored together with the decision result.

[0017] The system (i) enforces that sensitive decision-making takes place exclusively within a remotely attested secure domain; (ii) validates structure and semantics within limited latency; (iii) calculates risk for selected features without decrypting them; (iv) correlates multiple datasets to reveal causal motives; (v) trains models across clients without merging raw data; (vi) generates verifiable compliance artifacts without disclosing protected content; (vii) limits the operational scope using micro-quarantine zones; and (viii) provides reproducible, cryptographically verifiable provenance for every decision.

Claims

[1] Computer-implemented system for real-time validation and threat mitigation for structured health transactions, comprising: a client policy repository that contains client-specific validation rules, consent restrictions, and risk thresholds; a trusted policy execution enclave that only allows policies and models after remote attestation of code and configuration, and decrypts and processes protected fields exclusively within the enclave; a grammatically aware streaming validator that enforces format and semantic constraints and synthesizes an auto-repair suggestion in case of parser errors, transforming inputs into a policy-compliant form with minimal changes; a hybrid risk engine that calculates rule-based and learned risk scores and furthermore evaluates a subset of features using data protection-preserving calculations to determine risk contributions without decoding selected fields; a causal graph generator that correlates related transactions over time into an entity-resolved, time-aligned graph and identifies impossibility patterns with edge-wise responsibility values; a coordinator for federated learning who updates models from client-local aggregates under differential privacy budgets and suspends aggregation when a distribution drift is detected above a policy threshold; an audit subsystem that generates zero-knowledge evidence demonstrating that selected compliance predicates for a transaction have been met without disclosing protected health information; a risk-adaptive flow control system that directs high-risk substreams into micro-quarantine sections with enhanced testing while maintaining basic service quality for low-risk substreams; and a register that stores a validity context mark which links each decision to the identity of the policies, models, scope of consent and runtime environment used. [2] System according to claim 1, wherein the trusted policy execution enclave binds decryption keys to an attested measurement, rejects policy or model updates that do not match expected manifest hashes, and enters a pure rule fail-safe mode upon an integrity alarm. [3] System according to claim 1, wherein the audit subsystem, for each decision requiring verifiable compliance, issues a concise zero-knowledge proof that a predicate selected from the group consisting of inclusion of the scope of consent, compatibility of code and modifier, eligibility of the service provider type and compliance with the amount threshold is satisfied, wherein the proof and verifier reference are anchored in the register. [4] System according to claim 1, wherein the federated learning coordinator enforces client-specific budgets for differential data protection and includes a drift guard that stops cross-client aggregation when distribution drift or correlated anomalies are detected above a policy threshold. [5] System according to claim 1, further comprising a deception subsystem that inserts client-specific, non-clinical beacon fields into a regulated subset of transactions and, upon detection of such beacons outside authorized processing paths, triggers micro-quarantine routing, key revocation and incident logging.