Zero-knowledge inference sandbox (ZK-IS)

Abstract

A Zero-Knowledge Inference Sandbox provides a secure computational environment integrating cryptographic zero-knowledge proofs, secure multi­ party computation, and homomorphic encryption to execute i

Description

LifeMaitrix Fortress - Patent 8 Title Zero-Knowledge Inference Sandbox (ZK-IS) Inventors Alice Dyson and Christopher Daw Technical Field

[0001] The present invention relates to computer-implemented systems and methods for secure data analytics, particularly in environments requiring privacy-preserving inference and collaborative computation. More specifically, it concerns cryptographically verifiable sandbox architectures that integrate zero-knowledge proofs (ZKPs), homomorphic encryption, and secure multi-party computation (SMPC) to enable analytics on sensitive data without disclosing the underlying information. Background of the Invention

[0002] The proliferation of data-driven technologies—especially artificial intelligence (AI) and machine learning (ML)—has catalysed transformative improvements across sectors such as healthcare, finance, and personalised digital services. However, this increased capability has concurrently magnified the exposure of sensitive data to unauthorised access, breaches, and misuse. Conventional analytics systems generally require direct access to plaintext data, inherently violating modem privacy expectations and creating vulnerabilities in both centralised and decentralised settings.

[0003] To mitigate these risks, several cryptographic methods have emerged, notably homomorphic encryption, secure multi-party computation, and zero-knowledge proofs. While individually valuable, each method has critical limitations when applied in isolation within complex, real-time analytic environments.

[0004] Homomorphic encryption allows computation on encrypted data without decryption, preserving data confidentiality. However, fully homomorphic encryption (FHE) introduces substantial computational overhead and is typically impractical for real-time or large-scale applications. Even optimised partially or somewhat homomorphic schemes often restrict the types of permissible operations, requiring trade-offs between performance and analytical depth.

[0005] Secure multi-party computation (SMPC) enables multiple entities to compute on distributed inputs without disclosing individual data. While powerful in theory, SMPC suffers from latency, coordination challenges, and communication overhead. These limitations hinder scalability and render many SMPC implementations unsuitable for dynamic analytic contexts, especially where real-time consent enforcement and auditability are required.

[0006] Zero-knowledge proofs (ZKPs) provide strong cryptographic guarantees that a computation was correctly performed, without revealing the data used. Despite their potential, ZKPs are predominantly employed in narrow applications such as identity verification or blockchain validation. Existing frameworks rarely integrate ZKPs with broader privacy-preserving techniques, nor do they offer modular proof generation that can certify compliance with user-defined consent rules in complex analytic workflows.

[0007] Thus, current architectures fail to combine these techniques into a cohesive, scalable system that simultaneously supports real-time analytics, user consent enforcement, and cryptographic auditability. There remains a clear technical need for an integrated computational sandbox that provides robust privacy guarantees, full compliance traceability, and verifiable analytic outcomes across distributed, sensitive datasets. Solution Overview

[0010] The invention described herein—designated the Zero-Knowledge Inference Sandbox (ZK-IS)—addresses these limitations by providing an integrated, modular, and cryptographically secured computational environment. The ZK-IS enables secure, collaborative inference on sensitive or distributed datasets without requiring data decryption or disclosure.

[0011] The system comprises five core modules that operate in synchrony: • A Zero-Knowledge Proof Module (ZKP Module, 101), responsible for generating cryptographic proofs verifying the correctness of each inference computation. • A Secure Multi-Party Computation Engine (SMPC Engine, 102), enabling collaborative analytics across encrypted inputs from multiple parties without centralising or exposing raw data. • A Homomorphic Encryption Processing Layer (HE Layer, 103), supporting computations directly on encrypted datasets using performance-optimised partial homomorphic schemes. • A Consent Alignment Logger (104), enforcing user-defined consent constraints in real time before any computation is permitted to proceed. • A Cryptographic Audit Logging Module (105), recording all analytic actions and consent validations as immutable, tamper-evident log entries for later verification and compliance auditing.

[0012] These modules interoperate to form a unified computational framework in which data privacy, inference integrity, real-time consent compliance, and cryptographic auditability are simultaneously guaranteed. Each inference operation within the ZK-IS is permitted only if fully authorised under the stored consent parameters and is accompanied by a cryptographic proof of compliance and correctness. All computation events are logged using chained cryptographic hashes, enabling independent verifiability without compromising data confidentiality.

[0013] By combining proven cryptographic primitives—such as zk-SNARKs, threshold secret sharing, and homomorphic arithmetic—with a real-time consent validation and audit system, the invention delivers a comprehensive solution to longstanding challenges in privacy-preserving analytics. It enables scalable, verifiable inference in high-risk domains such as healthcare, finance, and personalised digital services where sensitive data must be protected, consent rigorously enforced, and compliance auditable under stringent regulatory regimes. Summary of the Invention

[0014] The present invention, designated the Zero-Knowledge Inference Sandbox (ZK-IS), provides a cryptographically secured, privacy-preserving computational environment for conducting inference and analytic operations on sensitive or distributed datasets without requiring raw data exposure. The system enables collaborative analytics and verifiable computation by integrating three complementary cryptographic techniques—zero-knowledge proofs (ZKPs), homomorphic encryption, and secure multi-party computation (SMPC)— within a unified architectural framework that ensures real-time consent enforcement and immutable auditability.

[0015] The architecture comprises a set of interoperable modules that collectively enforce compliance, privacy, and verifiability throughout the lifecycle of each analytic request. In one embodiment, the ZK-IS includes the following core components: • A Zero-Knowledge Proof Module (101), responsible for generating cryptographic proofs that demonstrate the correctness of analytic computations without exposing input data. The module enables verifiers—such as regulators, auditors, or compliance engines—to confirm computation integrity using succinct, non-interactive proofs that do not disclose the underlying data or internal operations. • A Secure Multi-Party Computation Engine (102), configured to support privacypreserving collaborative analytics among independent data holders. The engine ensures that computations are performed on encrypted or secret-shared data inputs without centralisation or mutual disclosure, maintaining the data sovereignty of each participant throughout the analytic process. • A Homomorphic Encryption Processing Layer (103), designed to enable efficient computation directly on encrypted data using optimised partially or somewhat homomorphic encryption techniques. This module supports inference tasks that require encrypted arithmetic or aggregation while maintaining strong performance and ensuring that decrypted results are never exposed unless permitted by consent constraints. • A Consent Alignment Logger (104), which dynamically verifies each analytic request against cryptographically stored user consent parameters. If a proposed operation does not align with the active consent rules, the system halts execution and logs the denial event for audit purposes. • A Cryptographic Audit Logging Module (105), which records all computation events, consent verifications, and proof objects as immutable entries in a tamper-evident audit ledger. These logs can be externally verified to demonstrate lawful compliance and operational integrity without revealing any sensitive data.

[0016] These modules are deployed within a sandboxed computational framework that orchestrates data intake, verification, encrypted processing, proof generation, and audit logging through secure inter-module communication channels. Each permitted inference operation is therefore mathematically provable, consent-aligned, and cryptographically logged—offering a complete and independently verifiable trail for regulatory and compliance use cases. Operational Advantages

[0017] The modules described above are cohesively integrated into a unified ZeroKnowledge Inference Sandbox (ZK-IS) framework, which orchestrates data flows and computational operations through secure, consent-aligned logic. Each analytic request within the sandbox is evaluated against cryptographically stored consent parameters, ensuring that only authorised operations are executed. Every computation is recorded in a tamper-evident audit ledger, accompanied by cryptographic proof artefacts, enabling full post-hoc verification of system behaviour without exposing sensitive data. This architecture provides a trusted foundation for regulated analytics across distributed, high-sensitivity data environments.

[0018] The ZK-IS delivers several key technical and operational advantages over conventional approaches: • Enhanced Data Privacy: All computations are performed on encrypted or secret-shared data, ensuring that raw input values remain inaccessible throughout processing. This design substantially reduces the attack surface for data breaches and aligns with stringent data protection regulations such as the UK GDPR and EU Data Act. • Computational Efficiency and Scalability: By leveraging partially and somewhat homomorphic encryption methods optimised for analytical workloads, in combination with succinct zero-knowledge proofs (e.g. zk-SNARKs), the invention achieves practical performance without reliance on fully homomorphic encryption (FHE). The architecture supports near-real-time analytics and scales from single-party use cases to complex, multi-entity collaborative workflows with acceptable performance characteristics. • Real-Time Consent Compliance Enforcement: The Consent Alignment Logger module ensures that no analytic action is performed without prior validation against stored, user-defined consent rules. Consent parameters may be revoked, amended, or scoped in real time, and all operations are dynamically evaluated for compliance prior to execution. This supports continuous lawful alignment in fast-changing regulatory or personal contexts. • Robust Auditability and Verifiability: Each computational event within the ZK-IS is logged with cryptographic integrity, using chained hash structures to produce a tamper-evident ledger. Verifiers may independently confirm that all outputs are both correct (via zero-knowledge proofs) and compliant (via consent validation logs), without requiring access to the underlying data. This satisfies transparency and accountability requirements in regulated domains such as finance, healthcare, and public sector analytics. • Seamless Multi-Party Collaboration: The system enables collaborative analytics across multiple data holders without centralising data or compromising confidentiality. SMPC protocols facilitate distributed computation while preserving each participant’s data sovereignty. This allows federated analysis to be conducted securely, with full mathematical assurances of privacy and correctness.

[0019] By holistically integrating advanced cryptographic computation, consent governance, and audit verification into a unified environment, the ZK-IS invention delivers a comprehensive solution to the most pressing privacy and compliance challenges in modern data-driven systems. It enables regulated entities, collaborative research bodies, and sensitive-data platforms to extract value from distributed data assets while maintaining strict adherence to privacy, consent, and audit standards at every stage of the analytic lifecycle. Brief Description of the Drawings

[0020] Embodiments of the invention are illustrated, by way of example only, in the accompanying drawings. Each module, process, or component is identified using a consistent series-based numbering system—100-series for Figure 1, 200-series for Figure 2, and so forth—corresponding to the detailed description and claims for clarity: • Figure 1 (Sheet 1 of 6) - Overall ZK-IS System Architecture (100-series): A schematic representation of the Zero-Knowledge Inference Sandbox, illustrating the integration and interconnection of five principal modules: the Zero-Knowledge Proof Module (101), Secure Multi-Party Computation Engine (102), Homomorphic Encryption Layer (103), Consent Alignment Logger (104), and Cryptographic Audit Logging Module (105). An External Verifier Interface (106) is also shown, indicating pathways for compliance verification. Interconnecting data and control flows are denoted using directional arrows to illustrate secure modular interactions. • Figure 2 (Sheet 2 of 6) - Zero-Knowledge Proof Module Operational Flow (200-series): A flow diagram depicting the core operational sequence of the ZKP Module. The diagram includes initiation of the proof generation process (201), execution of the cryptographic proof logic (202), and the verifier validation procedure (203). This figure demonstrates how zero-knowledge proofs are created and validated without revealing input data. • Figure 3 (Sheet 3 of 6) - Secure Multi-Party Computation Engine Process (300-series): An operational flowchart showing secure collaborative computation workflows within the SMPC Engine. It includes the intake of distributed encrypted data from participating entities (301), privacy-preserving computation execution (302), and secure aggregation of results (303), ensuring no individual data exposure during or after computation. • Figure 4 (Sheet 4 of 6) - Homomorphic Encryption Layer Workflow (400-series): A workflow diagram detailing encrypted data operations performed within the Homomorphic Encryption Layer. This includes encrypted data intake (401), inference processing within the encrypted domain (402), partial decryption steps (403), and secure result generation (404) for authorised outputs. The diagram illustrates how this layer manages analytic tasks entirely within the ciphertext domain. • Figure 5 (Sheet 5 of 6) - Consent Alignment and Audit Logging Process (500-series): A schematic view of the integrated real-time consent verification and audit logging workflow. Key steps include real-time consent validation (501), generation of a cryptographically linked consent log entry (502), and creation of immutable audit records (503) for each operation. This figure demonstrates how the system enforces dynamic consent governance and permanent auditability. • Figure 6 (Sheet 6 of 6) - Illustrative Secure Analytics Scenario - Healthcare (600-series): A contextualised diagram depicting an applied use case in healthcare. Encrypted inputs from multiple healthcare providers (601, 602) are analysed collaboratively using the sandbox (603), with results subjected to cryptographic audit verification (604). This figure showcases practical enforcement of privacy, consent, and accountability across organisational boundaries. Detailed Description of the Invention

[0021] The Zero-Knowledge Inference Sandbox (ZK-IS) is a secure computational framework designed to execute privacy-preserving analytics, inference operations, and collaborative computations on sensitive data without requiring access to raw information. The system integrates zero-knowledge proofs (ZKPs), secure multi-party computation (SMPC), and homomorphic encryption into a unified architectural environment that enforces real-time consent compliance and provides immutable auditability. This section provides an in-depth explanation of the system’s architecture and core module interactions, with reference to the accompanying drawings where applicable. System Components and Architecture (Figure 1)

[0022] Referring to Figure 1, the ZK-IS comprises several primary components, each responsible for a distinct layer of cryptographic assurance, compliance enforcement, or data handling. In an exemplary implementation, the five principal components are: • Zero-Knowledge Proof Module (101): This module implements cryptographic mechanisms to generate and verify proofs that confirm the correctness of analytic computations without exposing any sensitive data. It comprises: o A Proof Generation Subsystem, responsible for constructing zeroknowledge proofs that validate the computation performed. o A Proof Verification Interface, allowing internal or external verifiers to confirm the validity of proofs without access to the inputs. o A Consent Parameter Enforcement Layer, which ensures that only computations complying with pre-approved consent policies are eligible for successful proof generation.

[0023] The ZKP Module guarantees that each analytic output is mathematically certified to be the result of an authorised computation, without requiring exposure of either the computation's intermediate steps or original data inputs. • Secure Multi-Party Computation Engine (102): This engine enables collaborative computation among entities holding independent encrypted data. It includes: o A Distributed Data Input Manager, which securely receives and isolates encrypted inputs from different sources. o A Collaborative Computation Processor, which executes agreed-upon inference functions over secret-shared or encrypted datasets using secure function evaluation (SFE) or additive sharing protocols. o A Secure Aggregation Mechanism, which merges partial computation results into a final encrypted or privacy-preserving output, without revealing the individual data contributions of any participant.

[0024] The SMPC Engine ensures that multiple data holders can participate in secure analytics without compromising their respective data privacy or sovereignty. • Homomorphic Encryption Processing Layer (103): This layer enables encrypted data to be processed directly without decryption. It includes: o An Encrypted Data Input Interface, responsible for ingesting ciphertext datasets and validating their integrity. o An Inference Computation Module, which performs homomorphic operations (e.g. addition, multiplication) directly on encrypted values using partially or somewhat homomorphic encryption schemes selected for computational efficiency. o A Partial Decryption and Output Module, which performs tightly scoped decryption steps to reveal only those analytic outputs that are authorised by consent and audit policies.

[0025] The HE Layer supports analytic operations that can be conducted entirely in the encrypted domain, ensuring raw data never becomes accessible during any computation phase. • Consent Alignment Logger (104): This component is responsible for enforcing realtime user-defined consent constraints before computation is initiated. It comprises: o A Real-Time Consent Verification Logic, which checks incoming computational requests against stored cryptographically signed consent parameters. o A Cryptographic Consent Log, which records all consent evaluations— including timestamp, request metadata, decision result, and applicable policy ID—into a permanent, verifiable ledger.

[0026] If a proposed computation is not aligned with current consent permissions, the operation is blocked, and the event is logged for compliance review. This subsystem ensures that no action proceeds without lawful consent, even in the presence of valid computation logic. • Cryptographic Audit Logging Module (105): This module provides a complete, tamper-evident record of all system operations. It includes: o An Audit Trail Generation Subsystem, which produces cryptographically linked entries for each computational action, proof generation, and consent check. Entries are hashed and chained (e.g. using Merkle trees or SHA-256 digests) to preserve immutability. o An Audit Verification Interface, which allows authorised internal or external parties to review, export, and verify log entries independently, without accessing any protected data or sensitive parameters.

[0027] Collectively, this component provides the foundation for full regulatory compliance, enabling traceable validation of all analytic activity, including verification that each result originated from a permitted computation and was conducted under lawful consent. Functional and Operational Workflow

[0028] The components described above operate as a cohesive, modular system to enforce secure data analytics, privacy preservation, consent compliance, and cryptographic accountability. As illustrated in Figure 1, secure computation tasks are handled by the ZeroKnowledge Proof Module (101), Secure Multi-Party Computation Engine (102), and Homomorphic Encryption Layer (103), while real-time governance and verification are ensured by the Consent Alignment Logger (104) and the Cryptographic Audit Logging Module (105). These modules are interconnected by data and control flows (labelled 106), which transmit encrypted data inputs, verification signals, proof artefacts, and audit metadata between system layers. The architecture also interfaces with external verification parties via the Proof Verification Interface (within module 101) and the Audit Verification Interface (within module 105), enabling third-party assessment of operational correctness and compliance without requiring access to sensitive data.

[0029] A standard inference operation within the ZK-IS proceeds through the following high-level workflow, corresponding to the sequential stages illustrated across Figures 2 to 6: Step 1: Consent Verification and Data Input

[0030] Upon submission of a computational request to the sandbox, the Consent Alignment Logger (104) performs a real-time verification of the attached consent parameters. The request includes encrypted data inputs and a cryptographically signed declaration of user-defined constraints governing permissible use. If the request violates the applicable consent policy (e.g. requests unauthorised use of specific data fields or analysis types), execution is immediately denied, and an audit log entry is generated recording the denial, the timestamp, and the cause. Only when the request passes the consent verification step will the system ingest the encrypted data inputs for further processing. Step 2: Secure Multi-Party Data Management

[0031] If the analytic request involves multiple data holders, the SMPC Engine (102) coordinates secure intake. The Distributed Data Input Manager component receives independently encrypted datasets from each participating entity and validates integrity using cryptographic checksums or digital signatures. Each dataset is handled in isolation, with no intermediate decryption or central aggregation. Input data is prepared for collaborative computation through secret sharing or other SMPC-compatible formats. The confidentiality and autonomy of each participant’s data are preserved throughout this step. Step 3: Privacy-Preserving Inference Computation

[0032] Once inputs are validated, the analytic operation is executed in a privacy-preserving manner. Depending on the context, this occurs in one or both of the following environments: • Homomorphic Computation Mode (103): The Homomorphic Encryption Layer applies the inference function directly to encrypted data using partially or somewhat homomorphic operations (e.g. encrypted additions, multiplications). The intermediate states remain encrypted throughout, and the output is a ciphertext representing the result. • Secure Multi-Party Computation Mode (102): The SMPC Engine performs distributed secure computation using pre-agreed protocols (e.g. additive secret sharing, garbled circuits). Each participant processes their own encrypted or shared data, and the final result is assembled from the aggregated intermediate computations without revealing any raw values. In certain cases, the two modes are used in tandem—for example, to conduct partial computation homomorphically followed by SMPC-based result aggregation. At no point during this step is plaintext data exposed to any computation node. Step 4: Zero-Knowledge Proof Generation

[0033] After the computation is completed, the ZKP Module (101) is invoked to generate a cryptographic proof certifying that the operation was performed correctly and in alignment with active consent policies. The Proof Generation Subsystem constructs a zero-knowledge proof 7t that validates: • The mathematical integrity of the computation (i.e. the output is correctly derived from the inputs), • That no disallowed data fields or actions were involved, • That all input values were processed in accordance with the declared consent parameters. This proof may be verified by any authorised third party possessing the public verification key, without the need to inspect the inputs, algorithm internals, or outputs in plaintext. Secure Result Delivery and Final Verification

[0034] Following zero-knowledge proof generation, the system proceeds to final output preparation and delivery. If the computed result remains in encrypted or secret-shared form, the Partial Decryption and Output Module within the Homomorphic Encryption Layer (103) performs authorised decryption only on those result components explicitly permitted by the verified consent parameters. Decryption is limited in scope to preserve user privacy; for instance, aggregate statistics may be revealed while individual data points remain protected. The final analytic outputs—whether partially decrypted, encrypted, or aggregated—are securely transmitted to authorised recipients using encrypted channels.

[0035] Every output delivery event is recorded in the Cryptographic Audit Logging Module (105), including: recipient identifiers, result format (e.g. encrypted or decrypted), delivery time, and a reference to the associated consent verification and zero-knowledge proof. This ensures that every completed inference operation is end-to-end auditable. No output is transmitted unless a valid proof is linked, a corresponding consent verification is logged, and the audit ledger confirms that the result was derived from authorised operations only.

[0036] Throughout the entire computational pipeline, strict data-handling policies are enforced. All data remains encrypted at rest and in transit. Raw data never leaves the boundaries of the sandbox environment. No computation—regardless of scope—is performed without verified alignment to user-defined consent. Each operation is logged with cryptographic timestamps and hash links to prior actions, ensuring a permanent, immutable, and tamper-evident record. Together, these guarantees establish a trusted infrastructure for verifiable analytics, in which outputs are provably lawful, privacy-preserving, and regulatorily compliant—even in high-risk, multi-party analytic scenarios. Integration and Scalability

[0037] The ZK-IS system is designed with a modular, extensible architecture that accommodates future advancements in cryptographic technology and analytic complexity. Each major function—proof generation, secure computation, encrypted inference, consent governance, and audit logging—is implemented as a discrete component with defined inter module communication interfaces. This structure allows individual modules to be upgraded or substituted without requiring systemic redesign.

[0038] For example, the Zero-Knowledge Proof Module (101) may be updated to support emerging proof systems (e.g. zk-STARKs or Bulletproofs), or the Homomorphic Encryption Layer (103) may incorporate new partially homomorphic schemes offering improved performance. The modular configuration allows these innovations to be incorporated with minimal engineering overhead, enabling the system to maintain state-of-the-art cryptographic capability over time.

[0039] The ZK-IS is also engineered to support operational scalability. It can be deployed for single-user private analytics or large-scale, federated computations across multiple enterprises. The SMPC Engine (102) can distribute workload horizontally across computation nodes, and the homomorphic encryption tasks can be parallelised or accelerated using hardware optimisations such as FPGAs or GPUs. The audit logging mechanism supports high-throughput append-only storage models using distributed ledgers or blockchain-like chains to ensure fault tolerance and performance.

[0040] Crucially, all scaling strategies preserve the system’s core guarantees of privacy, consent enforcement, and auditability. Computation is always performed on protected data; consent rules are enforced in real time regardless of scale; and all operations remain subject to cryptographic proof and traceability. These properties make the ZK-IS suitable for deployment in dynamic, data-intensive environments—such as national healthcare infrastructures, financial regulation systems, and secure enterprise data analytics—where scale, trust, and verifiability must coexist. Operational Methods and Processes

[0041] The Zero-Knowledge Inference Sandbox (ZK-IS) implements clearly defined procedural methods to ensure privacy-preserving computation, consent-based governance, and cryptographically verifiable auditability. These methods describe how data requests, processing tasks, and validation events are handled internally. Each method corresponds to specific components and workflow stages as previously described and illustrated in Figures 1-6. 1. Consent Verification and Initialisation Method

[0042] • Step 1.1 - Receipt of Computational Request: A new inference request arrives at the Consent Alignment Logger (104). The request contains encrypted input data and a digitally signed consent policy associated with the dataset. This policy outlines the authorised analytic functions and permitted data usage context, digitally signed by the data controller or subject. • Step 1.2 - Consent Parameter Retrieval: The Consent Alignment Logger retrieves the applicable stored user-defined consent policy. Consent profiles are stored in a secure internal ledger indexed by data and user identifiers. The active policy governs the rules for data use, permissible algorithms, result access permissions, and revocation conditions. • Step 1.3 - Real-Time Consent Validation: The system performs cryptographic matching of the submitted consent declaration against the stored reference policy. This may involve signature verification and comparison of hashed consent elements (e.g. SHA-256(consent_request) versus SHA-256(reference_policy)), ensuring structural and semantic integrity. If the request is not compliant—such as requesting use of disallowed fields or operations—it is immediately rejected. • Step 1.4 - Consent Validation Recording: Regardless of result, the system logs the outcome in the audit ledger (105). The log entry includes a timestamp, request ID, validation result, reference hash to the consent profile used, and a digital signature confirming verification. This log entry is hash-chained to previous audit entries to form a tamper-proof, chronological trail. 2. Secure Data Intake and Preparation Method

[0043] • Step 2.1 - Data Input Acceptance: Upon successful consent validation, the encrypted data inputs are formally accepted for processing. Depending on the request type, data is routed to either the Homomorphic Encryption Processing Layer (103) or the Secure Multi-Party Computation Engine (102). For federated analytics, each data contributor uploads encrypted inputs separately, maintaining strict input segregation. • Step 2.2 - Input Validation and Integrity Checks: The system performs cryptographic integrity checks on each ciphertext input. Hash digests (e.g. using SHA-256) are computed and compared with stored reference values or included digital signatures. This prevents processing of corrupted, altered, or malicious data submissions. Validation succeeds only if both integrity and origin authenticity are confirmed.

[0044] • Step 2.3 - Data Isolation and Distribution (SMPC Context): In multi-party scenarios, data remains logically isolated. The SMPC Engine assigns secret shares or encryption keys to distributed computing nodes such that no node can reconstruct any full input independently. Secure communication channels and pre-configured computation protocols (e.g. additive secret sharing or threshold decryption) are used to initialise distributed processing. The system ensures strict compliance with isolation constraints while maintaining computational integrity.

[0045] These initialisation methods ensure that every analytic request is legally grounded, cryptographically validated, and securely prepared for execution. No input proceeds into the computational pipeline unless the associated consent profile is active, intact, and valid. Furthermore, all incoming data is rigorously verified for authenticity, and no sensitive information is shared across party boundaries at any point in the setup phase. 3. Privacy-Preserving Computation Method

[0047] • Step 3.1 - Computation Routing: The system controller evaluates the characteristics of each incoming request to determine the optimal computational pathway. Parameters such as number of participants, nature of the analytic function, performance requirements, and encryption compatibility are considered. If the request involves only a single data controller or a centralised dataset, and the computation is compatible with homomorphic operations, the task is routed to the Homomorphic Encryption Layer (103). If the request involves multiple distributed data contributors, the Secure Multi-Party Computation Engine (102) is selected. In some cases, hybrid execution may be used, with both environments contributing to different phases of the task. The routing logic ensures minimal risk exposure while maintaining computational efficiency.

[0048] • Step 3.2 - Encrypted Computation Execution (HE Layer): Within the HE Layer (103), computation is performed directly on ciphertext inputs. The system applies domain-specific inference algorithms that have been transformed to operate using homomorphic primitives (e.g. addition, multiplication). At no point are intermediate results decrypted or exposed. For example, when training a model, all coefficient updates or statistical operations are conducted on encrypted representations. The result of this computation is an encrypted output (e.g. encrypted prediction, encrypted model vector), which remains unintelligible until permitted for partial decryption. This guarantees that no plaintext data ever resides in system memory during execution.

[0049] • Step 3.3 - Collaborative Secure Computation (SMPC Engine): In federated or distributed cases, the SMPC Engine (102) executes the requested function using secret-sharing protocols or a blend of encryption and secure computation primitives. Each party operates on their own encrypted or secret-shared data without visibility into any other party’s information. Nodes exchange random-masked or encrypted intermediary values through secure channels during computation rounds. At no stage does any participant receive sufficient information to reconstruct another’s input. The output of this process is either a set of encrypted partial results or secret shares of the final analytic result.

[0050] • Step 3.4 - Result Aggregation: Once the distributed or encrypted computation is complete, the intermediate outputs—whether partial ciphertexts from the HE Layer or result shares from the SMPC Engine—are securely combined to form the final analytic result. In SMPC scenarios, this may involve reconstructing a shared value via threshold combination. In homomorphic contexts, encrypted outputs may be homomorphically added or merged without decryption. The outcome remains privacy-preserving and suitable for further cryptographic verification or controlled decryption in subsequent stages.

[0051] This method ensures that all computations within the ZK-IS environment preserve the confidentiality of inputs and intermediate states. No plaintext data is available to the system at any stage, and only the final authorised results may be partially decrypted or verified under strict consent-aligned conditions. 4. Zero-Knowledge Proof Generation Method

[0052] • Step 4.1 - Proof Circuit Initialisation: Once the computation result has been produced (either via the Homomorphic Encryption Layer or the SMPC Engine), the Zero-Knowledge Proof Module (101) initiates the proof generation process. This begins with the construction of a proof circuit—a formal, machine-readable mathematical representation of the computation that is to be cryptographically verified. The circuit includes: (i) the public inputs (e.g. encrypted output or a cryptographic hash thereof), (ii) the private witness (e.g. secret inputs or intermediate values), and (iii) the computation’s logical structure (e.g. permitted operations, consent constraints). If zk-SNARKs are used, the system loads the corresponding proving key and prepares the instance and witness variables for input into the prover.

[0053] • Step 4.2 - Generation of Zero-Knowledge Proof: The Proof Generation Engine constructs the actual cryptographic proof, denoted 7t. This proof demonstrates that a correct computation was carried out over permitted data, by a party in possession of the necessary secret inputs, without revealing any of those inputs. The proof 7t is compact—typically under 1 KB in size for zk-SNARKs—and non-interactive. It contains sufficient cryptographic commitments to guarantee that the computation adhered to all system rules, including enforcement of encoded consent constraints. For example, if the user's consent profile forbids use of certain data fields, the proof circuit will not validate unless those fields were demonstrably omitted or zeroed. This ensures that consent enforcement is not only policy-driven but also mathematically verifiable.

[0054] • Step 4.3 - Proof Delivery and Storage: Once generated, the zero-knowledge proof 7t is stored and distributed through two parallel mechanisms: o It is written to the Cryptographic Audit Logging Module (105), where it is hash-linked to the operation’s corresponding audit entry. This inclusion provides a verifiable, immutable record that the computation was valid and consent-compliant. o It is made accessible via the Proof Verification Interface. This allows authorised parties—such as external auditors, compliance regulators, or trusted internal systems—to independently verify the result. Proofs may be stored in a results registry, included as metadata alongside analytic outputs, or transmitted to external endpoints under encryption.

[0055] At the conclusion of this method, the system possesses a final analytic output (still encrypted or secret-shared) and a linked zero-knowledge proof 7t certifying correctness and compliance. This dual artefact—output plus proof—forms the core deliverable of any ZK-IS analytic operation, enabling full trust and verifiability without any requirement for data exposure or system-level introspection. 5. Audit Logging and Verification Method

[0056] • Step 5.1 - Cryptographic Audit Logging: The Cryptographic Audit Logging Module (105) generates a new audit entry corresponding to the completed analytic operation. This entry typically includes: a unique operation identifier, hashes referencing the encrypted input, the applied consent policy, the timestamp of execution, a hash of the encrypted output, and the zero-knowledge proof (k) or a hash reference thereof. The audit entry is denoted DiD iDi. It is concatenated with the previous entry’s hash Hi-lH_{i-l}Hi-l, along with a current timestamp TiT iTi, and then hashed to produce a new ledger hash: Hi=Hash(Di||Hi-l||Ti)H_i = \text{Hash}(D_i \parallel H_{i-1} \parallel T_i)Hi =Hash(Di||Hi—1 ||Ti) This process ensures cryptographic immutability of the log chain. Any attempt to remove, alter, or re-sequence entries would break the chain, rendering tampering immediately detectable.

[0057] • Step 5.2 - Audit Trail Accessibility: The audit ledger is made accessible to authorised parties via secure, read-only interfaces. For example, an external auditor may query the system’s audit API to retrieve all audit entries related to a given computation, data subject, or timeframe. Log storage may be implemented on a permissioned distributed ledger, append-only cryptographic database, or secured local archive. Interfaces may offer filtering and search functionality, while preserving access to raw cryptographic data for independent validation.

[0058] • Step 5.3 - Proof-Based Audit Verification: Auditors or oversight systems may use the audit data to verify three core properties: (i) correctness of computation (via zk-proof 7t), (ii) compliance with consent constraints (via consent hash reference), and (iii) log integrity (via chained hash validation). The zero-knowledge proof 7t is verified against the corresponding public verification key vkvkvk using: Verify(vk,x,7t)=l=>Computation is Valid\text{Verify}(vk, x, \pi) = 1 \Rightarrow \text{Computation is Valid} Verify(vk,x,7t)=l ^Computation is Valid If verification fails or if the audit chain is broken, an alert is raised for further investigation. This enables full accountability and compliance without requiring access to any sensitive data. 6. Secure Result Delivery Method

[0059] • Step 6.1 - Partial Decryption and Consent Alignment Check: Prior to result delivery, the system conducts a final check against consent boundaries. The Homomorphic Encryption Layer (103) uses the relevant decryption key or key shares (in threshold configurations) to decrypt only the permitted portions of the result. For instance, if consent permits access only to aggregated data, individual data points remain encrypted. The system halts and logs any attempt to extract results beyond the authorised scope, ensuring post-computation consent compliance.

[0060] • Step 6.2 - Secure Output Generation: The final result—whether fully encrypted, partially decrypted, or fully decrypted as allowed—is then assembled for delivery. This output is accompanied by metadata including: the associated audit log hash, proof identifier, and optional machine-verifiable compliance attestation. All plaintext portions are securely wiped from memory post-transmission, and encrypted transport channels (e.g. TLS 1.3) are used for all outbound communication.

[0061] • Step 6.3 - Authorised Result Distribution: Only recipients matching the approved access list within the consent parameters are allowed to retrieve the result. Distribution may occur via secure API endpoints, encrypted file delivery, or system-to-system integration. The system appends a digital attestation to each delivery, for example: “This result was produced by ZK-IS under operation ID X, governed by consent profile Y, and validated under proof 7t. Signed: ZK-IS Authority Key.” Recipients can verify the attestation using the public key of the ZK-IS issuer or operator.

[0062] • Step 6.4 - Output Audit Logging: The final delivery step is itself recorded in the audit trail. The log entry includes: recipient ID, delivery timestamp, output hash, delivery modality (e.g. API, file), and the associated consent enforcement reference. This closes the operational cycle, enabling full traceability not only of computation but also of output access and dissemination. With this, the analytic request is cryptographically logged from initiation to completion, providing continuous, mathematically enforceable guarantees of compliance, privacy, and transparency. 7. Continuous Operational Integrity and Compliance Monitoring

[0063] The Zero-Knowledge Inference Sandbox (ZK-IS) continuously monitors all system actions to maintain strict compliance with consent policies, data privacy requirements, and analytic integrity. The Consent Alignment Logger (104) and the Cryptographic Audit Logging Module (105) operate in tandem to detect and respond to any deviation from expected operational behaviour in real time.

[0064] If an unauthorised action is attempted—such as an internal module requesting access to raw data, or an external entity issuing a retrieval query not permitted by the consent profile—the system automatically intervenes. It blocks the operation, logs the incident in the audit trail with a timestamp and incident code, and may escalate an alert to authorised system administrators. This includes situations such as: • Detection of a malformed message or misbehaving node in a secure multi-party computation session, • An attempt to compute on data whose consent has been revoked mid-process, • Inconsistencies between proof generation and logged consent validation references.

[0065] The architecture assumes a conservative threat model in which some internal or external entities may become faulty or adversarial. To mitigate such risks, all system modules verify each other’s actions through embedded cross-references. For example, the ZKP Module (101) will only generate proofs for computations that have a valid, timestamped consent validation recorded in module 105. This verification ensures that no proof—no matter how well-formed—is accepted unless it matches a permitted and auditable event.

[0066] By design, the ZK-IS eliminates the need to trust any individual system component. Instead, it distributes trust across cryptographic enforcement mechanisms and cross-verified logs, ensuring that the correctness, legality, and privacy of every operation are mathematically verifiable. This approach provides strong assurance of operational integrity and regulatory compliance even in untrusted or semi-trusted environments. Algorithms and Mathematical Representations

[0067] The Zero-Knowledge Inference Sandbox (ZK-IS) relies on a suite of cryptographically grounded algorithms and computational models to deliver its core capabilities—namely secure computation, consent validation, and verifiable auditability. The following subsections define key algorithms and their representative formulations. Zero-Knowledge Proof (ZKP) Algorithms

[0068] ZK-IS employs succinct non-interactive zero-knowledge proofs, such as zk-SNARKs, to verify correctness of inference computations without revealing underlying data. • Proof Generation: A prover PPP holding secret input www and a proving key pkpkpk constructs a cryptographic proof 7t\pi7t for a public statement xxx, confirming that xxx can be derived correctly from www: 7t<—Prove(pk,x,w)\pi Meftarrow \text{Prove}(pk, x, w)7t<—Prove(pk,x,w) • Proof V erification: A verifier VW uses the corresponding verification key vkvkvk to check whether 7t\pi7t is a valid proof that xxx was computed correctly: Verify(vk,x,7t)={lif validOotherwise\text{Verify}(vk, x, \pi) = \begin{cases} 1 &\text{if valid} \\ 0 &\text{otherwise} \end{cases}Verify(vk,x,7t)={10if validotherwise Verification succeeds without exposing any part of the private witness www, maintaining full confidentiality of the underlying computation. Secure Multi-Party Computation (SMPC) Algorithm

[0069] ZK-IS uses additive secret-sharing SMPC protocols to enable distributed computation without input disclosure. • Secret Sharing Phase: A secret sss is divided into nnn shares: {sl,s2,...,sn}\{ s_l, s_2, \ldots, s_n\}{sl,s2,...,sn} such that no individual share reveals meaningful information about sss. • Evaluation Phase: Each party computes a local function f (si)f (s_i)f (si) over its share: f(s)=£i=lnf'(si)f(s) = \sum_{i=l}A{n} f(s_i)f(s)=i= l£nf(si) • Reconstruction Phase: The system aggregates the local results to reconstruct the full computation result, without disclosing individual inputs. Homomorphic Encryption (HE) Computational Model

[0070] ZK-IS utilises partially or somewhat homomorphic encryption (PHE / SWHE) schemes for efficient encrypted computation. • Encryption: A plaintext mmm is encrypted with a public key pkpkpk: c=Encrypt(m,pk)c = \text{Encrypt}(m, pk)c=Encrypt(m,pk) • Additive Homomorphism: Given cl=Encrypt(ml,pk)c_l = \text{Encrypt}(m_l, pk)cl=Encrypt(ml,pk) and c2=Encrypt(m2,pk)c_2 = \text{Encrypt}(m_2, pk)c2=Encrypt(m2,pk), then: csum=c l ©c2=Encrypt(m l+m2,pk)c_[\text{ sum}} = c_l \oplus c_2 = \text{Encrypt}(m_l + m_2, pk)csum=cl®c2=Encrypt(ml+m2,pk) • Multiplicative Homomorphism (if supported): cprod=cl0c2=Encrypt(ml-m2,pk)c_{\text{prod}} = c_l \otimes c_2 = \text{Encrypt}(m_l \cdot m_2, pk)cprod=cl0c2=Encrypt(ml-m2,pk) • Partial Decryption: With threshold decryption: mpartial=PartialDecrypt(c,skpartial)m_{\text{partial} } = \text{PartialDecrypt}(c, sk_{\text{partial}})mpartial=PartialDecrypt(c,skpartial) Multiple shares are combined to recover full plaintext mmm. Consent Alignment and Validation Algorithm

[0071] To enforce policy-level governance of data use, ZK-IS incorporates signature-based consent verification: • User Signature on Consent Metadata: SigConsent=Sign(hM,skConsent)\text{Sig}_{\text{ Consent}} = \text{Sign}(h_M, sk_{\text{Consent}})SigConsent=Sign(hM,skConsent) where hM=Hash(Consent Metadata)h_M = \text{Hash}(\text{Consent Metadata})hM =Hash(Consent Metadata) and skConsentsk_{\text{Consent} }skConsent is the private signing key. • Real-Time Consent Validation: VerifyConsent(hM, SigConsent,pkConsent)=true\text{VerifyConsent}(h_M, \text{Sig}_{\text{Consent}}, pk_{\text{Consent}}) = \text{true}VerifyConsent(hM , SigConsent, pkConsent)=true if and only if the consent signature is cryptographically valid. This process ensures that only operations explicitly authorised by the data subject or owner are permitted to execute. Cryptographic Audit Logging Algorithm

[0072] Audit records are linked using a secure hash chain to produce a tamper-evident ledger. • Log Entry Construction: Given current audit data DiDiDi and timestamp TiTiTi, and previous hash Ai-1 A_{i-1} Ai-1: Ai=H(Ai-l ||Ti||Di)A_i = H(A_{i-l} \parallel T_i \parallel D_i)Ai=H(Ai-1 ||Ti||Di) • Audit Log Verification: An external verifier recomputes hashes across all entries: An=H(H(.. ,H(AO||T1 ||D1).. ,)||Tn||Dn)A_n = H(H(\ldots H(A_0 \parallel T_1 \parallel D_l) \ldots ) \parallel T n \parallel D_n)An=H(H(.. ,H(AO||T1 ||D 1).. ,)||Tn||Dn) A mismatch in any chain link indicates tampering or data corruption.

[0073] Collectively, these algorithms form the cryptographic foundation of the ZK-IS system. They enable all analytic processes to be securely performed, independently verifiable, and tightly bound to consent and audit policies. Each algorithm has been selected or adapted to ensure compatibility with practical deployment requirements—balancing strong security with computational efficiency. Implementation Examples and Scenarios

[0074] To demonstrate the practical utility and versatility of the Zero-Knowledge Inference Sandbox (ZK-IS), several real-world implementation scenarios are provided. Each example illustrates how the system enables secure, compliant analytics that would otherwise pose privacy, sovereignty, or regulatory challenges. In every case, ZK-IS ensures that sensitive data remains protected, consent parameters are rigorously enforced, and all computations are provably correct and auditable using cryptographic proof artefacts. • Example Scenario 1 - Healthcare Data Analytics

[0075] A group of hospitals and clinics collaborates to predict regional disease trends by analysing aggregated patient data. The ZK-IS enables federated computation using SMPC and HE, allowing each institution to contribute encrypted or secret-shared patient records without centralising or revealing any sensitive information. Patient consent is individually verified for each data record prior to participation, and the resulting inference is accompanied by a zero-knowledge proof verifying both analytic correctness and compliance. This allows regulators or research auditors to validate the analysis without accessing medical records. • Example Scenario 2 - Financial Fraud Detection

[0076] A consortium of financial institutions uses ZK-IS to detect anomalous transaction patterns across distributed banking systems. Each bank retains full control of its customer data, which is encrypted and processed using homomorphic aggregation and distributed SMPC protocols. The system identifies coordinated fraud attempts by analysing aggregate behavioural indicators. Results are submitted to regulatory authorities with accompanying ZKPs, allowing full verification of methodology and compliance without disclosing proprietary financial data or personally identifiable information. • Example Scenario 3 - Smart City Infrastructure Monitoring

[0077] Private infrastructure vendors and municipal agencies use ZK-IS to jointly compute environmental and transportation analytics, such as city-wide air quality indices or traffic congestion patterns. Each party submits encrypted sensor data (e.g. emissions, noise, location metrics), which the system processes using homomorphic encryption and SMPC. No vendor data is exposed to other participants. The final results are verifiably correct, supported by cryptographic audit logs and zero-knowledge proofs, and can be shared publicly or used for policy interventions. • Example Scenario 4 - Personalised Digital Marketing Analytics

[0078] A federation of online retailers wishes to deliver personalised marketing based on cross-platform shopping behaviour. ZK-IS enables collaborative profiling through joint computation across customer purchase histories, search activity, and preference data— without centralising or disclosing individual data points. Customers explicitly opt in to such analysis through verifiable consent signatures. The system returns segmentation insights to each retailer only for their respective customer base, and all computations are provably aligned with customer-specific consent policies using ZKPs. • Example Scenario 5 - Cross-Border Regulatory Compliance Audits

[0079] A multinational corporation subject to data residency laws in multiple jurisdictions uses ZK-IS to satisfy external regulatory audits. Rather than transferring sensitive records across borders, the company submits proof objects derived from local computations—such as zero-knowledge proofs of compliance with data handling policies and audit ledger digests. Regulators in each country are able to independently validate results against policy and procedural benchmarks without accessing underlying data, preserving both legal sovereignty and operational confidentiality. Glossary of Terms • Audit Logging Module (105): A component of ZK-IS responsible for cryptographically recording computational events, consent verification outcomes, and other significant actions. It ensures transparency and verifiability by creating an immutable ledger of events, typically using hash-chaining. • Consent Alignment Logger (104): A security module within ZK-IS that verifies incoming computational requests in real time against predefined user consent parameters. It ensures that each action complies with consent rules and logs the validation outcome securely. • Cryptographic Hashing: A one-way function that transforms data into a fixed-length digest. Used in ZK-IS for ensuring data integrity, chaining audit log entries, and anonymising sensitive values without revealing them. • Distributed Data Input Handler: A subsystem of the SMPC Engine responsible for securely receiving and isolating encrypted input data from multiple independent sources. • Encrypted Data Intake Subsystem: A component of the Homomorphic Encryption Processing Layer (103) that securely ingests, authenticates, and validates encrypted datasets. • Fully Homomorphic Encryption (FHE): A cryptographic scheme that allows arbitrary computations on encrypted data. ZK-IS avoids the performance overhead of FHE by using optimised partially or somewhat homomorphic methods. • Homomorphic Encryption (HE): A class of encryption schemes that support computation directly on ciphertexts, yielding encrypted results that correspond to operations performed on the plaintext. • Immutable Audit Ledger: The append-only data structure maintained by the Audit Logging Module to record operations. Entries are chained using cryptographic hashes to guarantee tamper-evidence. • Partial Decryption: A controlled operation that decrypts only authorised portions of data or requires multiple key shares for full decryption, used to protect result confidentiality. • Privacy-Preserving Computation: Any computational method that ensures no sensitive information is exposed during execution. ZK-IS employs HE, SMPC, and ZKPs for this purpose. • Proof Circuit (ZKP): The formal representation of the computation and constraints being proven in a zero-knowledge proof. • Secure Aggregation Mechanism: A cryptographic process within the SMPC Engine for combining intermediate results securely and producing a final output without revealing individual inputs. • Secure Multi-Party Computation (SMPC): A cryptographic technique that allows multiple parties to jointly compute a function over their inputs without revealing those inputs to each other. • Threshold Additive Secret Sharing: A method of dividing a secret into shares such that a threshold number of shares can reconstruct the secret, while any smaller number reveals nothing. • Zero-Knowledge Proof (ZKP): A cryptographic protocol that allows a prover to demonstrate knowledge of a valid computation or fact without revealing any underlying data. • Zero-Knowledge Proof Module (101): The system component in ZK-IS responsible for generating and verifying zero-knowledge proofs of correct and consent-compliant computation. • zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge): A type of ZKP that is compact, fast to verify, and requires no interaction between prover and verifier. • Zero-Knowledge Inference Sandbox (ZK-IS): The system described in this patent—an integrated, modular, privacy-preserving computational environment enabling secure analytics on encrypted data while ensuring compliance with user consent and auditability requirements.

Claims

System Claims1. A computer-implemented system for performing secure, privacy-preserving inference computations without direct exposure of underlying sensitive data, comprising:o (a) a Zero-Knowledge Proof Module configured to generate cryptographic zero-knowledge proofs validating the correctness of inference computations without revealing underlying data inputs;o (b) a Secure Multi-Party Computation (SMPC) Engine configured to facilitate collaborative analytics across multiple encrypted data sources, maintaining data isolation and privacy for each source;o (c) a Homomorphic Encryption Processing Layer configured to execute inference computations directly on encrypted datasets, ensuring data remains encrypted during all computational steps;o (d) a Consent Alignment Logger configured to verify computational requests in real-time against user-defined, cryptographically stored consent parameters and to enforce strict data-use compliance based on these parameters; ando (e) a Cryptographic Audit Logging Module configured to record cryptographically secured audit entries of computational events, consent verifications, and generated proofs, thereby providing an immutable, transparent audit trail of all operations.

2. The system of claim 1, wherein the Zero-Knowledge Proof Module comprises:o (a) a proof generation engine for creating cryptographic zero-knowledge proofs of computational correctness and policy compliance; ando (b) a proof verification interface accessible by internal or external auditors to verify, without data disclosure, the validity of said proofs against public verification keys.

3. The system of claim 1, wherein the Secure Multi-Party Computation Engine comprises:o (a) a distributed data input handler for secure acceptance and isolation of encrypted inputs from multiple independent data sources;o (b) a collaborative computation module that executes inference algorithms across said encrypted inputs without revealing individual data; ando (c) a secure result aggregator that combines computation results into a final output without compromising the privacy of any input data.

4. The system of claim 1, wherein the Homomorphic Encryption Processing Layer comprises:o (a) an encrypted data intake subsystem for securely ingesting and validating encrypted datasets;o (b) an encrypted computation engine for executing inference computations directly within the encrypted domain of the data; ando (c) a partial decryption and output subsystem configured to perform controlled partial decryptions of computation results strictly according to verified consent constraints.

5. The system of claim 1, wherein the Consent Alignment Logger comprises:o (a) a real-time consent verification unit for dynamically validating each computational request against stored cryptographic consent parameters before execution;o (b) a cryptographic consent record store maintaining immutable records of consent validations and parameters; ando (c) a consent enforcement gateway that automatically blocks or halts any computation failing said consent validation, logging such events for audit.

6. The system of claim 1, wherein the Cryptographic Audit Logging Module comprises: o (a) an audit log generation subsystem creating cryptographically linked audit entries for each significant operation;o (b) an audit log verification interface providing secure access for external compliance audits to verify the integrity of the audit trail; ando (c) an immutable audit ledger ensuring secure, permanent, and tamper-proof storage of all audit entries.

7. The system of claim 1, wherein the cryptographic zero-knowledge proofs comprise zk-SNARK proofs that verify both computational accuracy and compliance with consent policies without revealing any sensitive data or intermediate values of the computation.

8. The system of claim 1, wherein the homomorphic encryption scheme utilised is partially or somewhat homomorphic, enabling computational efficiency and near-realtime analytics performance by supporting required operations (additions and / or limited multiplications) on encrypted data with significantly reduced overhead compared to fully homomorphic encryption.

9. The system of claim 1, further comprising an adaptive cryptographic framework that is configurable to integrate emerging cryptographic techniques and algorithms, thereby allowing the system to scale and remain future-proof as new privacypreserving computation methods are developed.Method Claims10. A method for securely executing inference computations within a computational environment, comprising the steps of(a) receiving a computational request containing encrypted data inputs and digitally signed consent parameters associated with said data;(b) verifying the received computational request in real-time against cryptographically stored user-defined consent parameters to ensure the request adheres to permitted data usage policies;(c) securely processing the encrypted data inputs using at least one of homomorphic encryption methods and secure multi-party computation methods, such that computations occur without decrypting sensitive underlying data;(d) generating cryptographic zero-knowledge proofs that verify the correctness and compliance of the executed inference computations (including adherence to consent constraints) without revealing any sensitive data;(e) cryptographically logging computational events, consent verification outcomes, and said zero-knowledge proofs to create an immutable audit trail; and(f) securely delivering the inference computation results to authorised recipients, wherein the delivered results are aligned with the applicable consent rules (with any disallowed information remaining encrypted or omitted).

11. The method of claim 10, wherein the consent verification step (step (b)) utilises cryptographic hashing and digital signature verification to ensure the integrity and validity of consent records, such that only requests with matching cryptographic consent signatures proceed to execution.

12. The method of claim 10, wherein the step of securely processing encrypted data inputs (step (c)) is performed using homomorphic encryption techniques that allow computations on data without decrypting it, so that no plaintext data is exposed during processing.

13. The method of claim 10, wherein multi-party inference computations are executed by distributing secret shares of data among multiple computing nodes using threshold additive secret sharing, and performing calculations on said shares such that the data remains secret throughout and only the final result can be reconstructed (as a protected or encrypted value).

14. The method of claim 10, wherein the cryptographic logging of audit data (step (e)) includes computing hash-chained audit entries for each event, thereby enabling verification of the audit trail’s integrity (any alteration of an entry breaks the hash chain) and ensuring the log remains tamper-proof and compliant with regulatory audit standards.Environment Claims15. A privacy-preserving, consent-driven computational environment, comprising integrated modules of zero-knowledge proof generation, secure multi-party computation, homomorphic encryption processing, consent parameter verification, and cryptographic audit logging, wherein each computational step is cryptographically verified for accuracy, compliance with user consent, and privacy assurance, thereby enabling data analytics and inference to be performed with strong privacy guarantees and auditability without exposing raw data.

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