Suresh Jaganathan, Venkatavara Prasad D, Aditya Krishna P, A Karthik
Health insurance claims processing and data storage pose challenges for security, efficiency, and transparency. Traditional distributed databases often rely on centralized management systems and enterprise-grade hardware, which can be costly and vulnerable. In contrast, Blockchain technology offers a decentralized approach to data management, ensuring transparency, security, and record immutability without requiring extensive hardware infrastructure. This paper examines the feasibility of leveraging blockchain, specifically the Internet Computer Blockchain (DFINITY), to automate health insurance claims processing and securely store insurance data. Additionally, a time-efficient algorithm is proposed to enhance querying and updating of insurance claims on the blockchain.
Abstract Hybrid architectures in permissioned blockchains combining on-chain policies with Threshold Proxy Re-Encryption (TPRE) suffer from a structural audit gap: cryptographic enforcement is decoupled from the ledger's state. This allows Byzantine proxies to execute unaccountable data transformations (ghost requests). Furthermore, traditional TPRE access revocation incurs O(N) linear overhead, creating a scalability bottleneck.To address these flaws, we propose DT-Share, a state-bound access control middleware. By leveraging a novel State-Bound Evidence mechanism, DT-Share cryptographically anchors TPRE outputs to the global ledger epoch. This transforms access revocation from an O(N) key-management task into a constant-time O(1) ledger state transition.A full-stack implementation using Hyperledger Fabric and IPFS demonstrates that DT-Share guarantees strict accountability with negligible overhead. Under high-concurrency workloads, it exhibits an elasticity inversion phenomenon, adaptively scaling throughput during bursty requests to provide a highly scalable, Byzantine-resistant middleware for distributed consortia.
Bilateral attribute-based access control for data trading must hide policies, provide cryptographic fairness, and avoid trusted third parties. Existing solutions either leak policy information, incur super-linear costs, or rely on trusted dispute resolution. We present PriME-Deal, a non-interactive protocol that simultaneously achieves policy-hiding bilateral matching, efficient threshold access control, and auditable fair exchange on public blockchains. The seller embeds a secret token under the buyer policy into an oblivious key-value store with pseudorandom masking; the buyer reconstructs the token locally via tag-based probing, eliminating combinatorial enumeration, and proves correctness in zero-knowledge. Fair exchange is enforced through a collateralized on-chain reveal with a cryptographic audit that penalizes misbehaviour without trusted parties. We prove security in the Universal Composability framework under standard assumptions. Compared with the state-of-the-art threshold fuzzy IB-ME scheme, the seller's publishing time is reduced by two orders of magnitude (e.g., 8.76s vs. 690s for a policy of 500 attributes). For a typical configuration of (200,20,5), the buyer completes token reconstruction and proof generation in 8.9s, with the zero-knowledge proof taking under 0.6s and remaining constant across all parameter scales. The on-chain cost is approximately 28.6M gas, well within Ethereum's block limit. PriME-Deal thus delivers the first practical privacy-preserving data trading protocol that combines linear seller overhead, bilateral policy hiding, and auditable fairness.
Data privacy concerns have become more critical than ever as machine learning and applied intelligence systems permeate sensitive industries such as healthcare, finance, national security, and personal services. This necessitates the development of privacy-preserving strategies for protecting private information while retaining the utility of intelligent models. This survey provides a comprehensive overview of privacy-preserving machine learning, with an emphasis on the cryptographic and statistical methods that are transforming how safe learning systems are built. The study starts by examining the most important components of the machine learning model and figuring out which of these may be protected to solve important privacy problems. The article then explores modern cryptographic techniques, including homomorphic encryption, zero-knowledge proofs, secure multiparty computations, and a statistical approach called differential privacy, that support contemporary privacy-preserving machine learning solutions. The study then explores how these strategies are applied independently and in hybrid systems to achieve accuracy, efficiency, and balance of privacy. This survey provides promising direction for protecting sensitive information during real-world model training and inference, offering insights into the design of trustworthy applied intelligence systems.
沈清欢, L Chen, Jimin Chen, Tao Li · 6 authors
Blockchain oracles bridge on-chain smart contracts and off-chain data sources, but encrypted off-chain data still raises two practical challenges: how to verify retrieval integrity without exposing sensitive values, and how to keep verification information fresh when the off-chain data set changes. Existing oracle and outsourced-database retrieval mechanisms often rely on plaintext verification, heavy cryptographic proofs, or static authentication structures, which limits their applicability to latency-sensitive IoT and decentralized finance scenarios. To address these issues, this paper proposes a retrieval integrity verification mechanism based on CKKS approximate homomorphic encryption and an authenticated index named CKKS-Auth Tree. The proposed mechanism verifies encrypted query results through homomorphically aggregated metadata, while smart contracts record versioned verification commitments to detect stale or replayed results after updates. The scope of the mechanism is the integrity, completeness, privacy, and freshness of data after commitment and upload; verifying the physical authenticity of the original data source is outside the core threat model. Experimental results show that the proposed scheme reduces authentication and verification overhead compared with existing retrieval verification methods while supporting encrypted metadata updates and on-chain synchronization.
The impending arrival of cryptographically relevant quantum computing threatens classical public‑key infrastructures. This paper reviews the latest developments (2025–2026) in post‑quantum cryptography (PQC), fully homomorphic encryption (FHE), and zero‑knowledge proofs (ZKP). NIST has advanced nine signature candidates to its third evaluation round and selected HQC as a backup encryption standard. Novel primitives include bio‑inspired RNA‑based cryptography, algebraic hash signatures, and topology‑mined lattice schemes. FHE has reached its fifth generation with the GL scheme and the MadPanthera virtual processor, while lightweight ZKPs such as Microsoft’s Vega enable mobile‑friendly verification. These advances demonstrate rapid maturation toward deployable quantum‑safe systems.
This paper presents TrustBridge, the first universal decentralized trust protocol integrating multi-agent LLM consensus, zero-knowledge proof generation, blockchain attestation, and natural language accessibility for real-world credential verification across employment, education, healthcare, and supply chain domains. The multi-agent consensus engine runs three independent Claude Sonnet instances in parallel, achieving 92% adversarial detection on a controlled test set — a 30 percentage point improvement over single-agent architectures. Zero-knowledge commitment schemes allow claimants to prove credential properties without revealing private values. An ERC-721 NFT certificate provides immutable on-chain attestation. This is Paper 1 of a planned two-paper series. Paper 2 will report large-scale deployment results and full ZK-SNARK integration. Targeting: IEEE Blockchain 2027
The traditional ways of handling academic credentials are considered inefficient, expensive, and very vulnerable to fraud and data alteration as they rely on single-point databases. To counter these drawbacks, the authors of this paper propose a novel conception relying on blockchain technology with its central traits such as decentralization, immutable state, and cryptographic security. Under such framework, all the academic credentials are stored in a distributed ledger as non-variable and visible entries, where each credential is securely encrypted, stamped with the time of its creation, and linked in an irreversible chain, thus practically eliminating the possibility of their falsification or unauthorized change without the agreement of the entire network. The verification process is decentralized so that employers, educational institutions, and students can instantly and directly authenticate the credentials through the blockchain thereby cutting the intermediaries and considerably shortening the administrative delays and reducing overheads. Moreover, smart contracts contribute to further efficiency by automatically taking care of the issuance, management, and verification of credentials according to the pre-defined rules, thus ensuring consistency and accuracy. However, the system still offers the highest user control and privacy through the provision of tools like digital wallets and decentralized identifiers for the students to own and manage their digital credentials. These tools also give the students power to decide who can access their records and under what conditions. To ensure the integrity and confidentiality of the data, advanced security technologies such as cryptographic hashing and zero-knowledge proofs are deployed while still allowing transparency to the process of verification.
Archy Renaldy Pratama Nugraha, Yandra Arkeman, Irman Hermadi, Yani Nurhadryani
Digital identity verification in e-governance faces a trilemma between security, scalability, and regulatory compliance with Indonesia's Personal Data Protection Law (UU PDP). To resolve this, in this paper, we propose the ZMC-Framework, a blockchain-based hybrid architecture integrating Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification and Merkle Trees for efficient, scalable data integrity on-chain. Its core innovation is a Legal Proof Protocol with 3+1 parameter augmentation, which cryptographically binds static identifiers to a user-controlled secret, ensuring compliance with UU PDP (data minimization) and UU ITE (authentication integrity) while aligning with key controls of the international ISO/IEC 27001:2022 standard. Evaluated on Polygon Mainnet, the framework demonstrates 29.9% lower operational costs for batch verifications and 50% better storage efficiency compared to pure ZKP systems. These results validate a practical solution to the verification trilemma, providing a secure, scalable, and legally sound foundation for public service identity management in Indonesia's digital governance ecosystem.
Donggoo Kim, Rajesh Upadhayaya, Milosz Bator, Tao Le
Proof of Reserves (PoR) enables centralized crypto exchanges to demonstrate that on-chain reserves are sufficient to cover customer liabilities. However, existing approaches, including Merkle-tree-based proofs and zero-knowledge PoR systems, remain difficult for everyday users to verify in practice, resulting in limited participation and weakened transparency. We introduce LPOR, a layered, usability-focused PoR framework that separates lightweight user-side checks from auditor-level cryptographic verification, enabling non-technical users to verify inclusion and publicly recompute total liabilities with minimal friction. By lowering verification barriers, LPOR increases user participation and substantially improves the probability of detecting omitted liabilities. We evaluate its scalability and omission detectability at a multi-million-user scale.
S. Praveena, T. Arasulingam, M. Dineshkumar, P. Puvirajan · 7 authors
The rapid expansion of digital commerce has brought forward new challenges in payment security and transactional trust. Buyers and sellers engaging in online platforms face persistent threats such as payment fraud, unauthorized fund diversions, delayed settlements, and an overreliance on centralized financial intermediaries. Traditional mechanisms, which route payments through banks and payment gateway providers, often introduce additional costs while creating points of vulnerability that undermine consumer confidence. This paper proposes a blockchain-driven decentralized crypto escrow payment framework designed to address these shortcomings in a fundamental way. Rather than routing buyer payments directly to merchant accounts, the system temporarily secures those funds within a smart contract-governed escrow until all agreed-upon transaction conditions have been satisfied — including verified order fulfilment and successful product delivery. In the event of a dispute or transaction failure, the system enforces pre-coded refund protocols without requiring manual intervention. The proposed framework is expected to strengthen the relationship between buyers and sellers, meaningfully raise the bar for payment security, and deliver a transparent, auditable transaction environment through the principles of decentralized finance.
Bitcoin is permissionless and does not rely on any central administrator, which gives it strong censorship resistance. At the same time, it is important to incentivize miners to behave in ways that align with the interests of the system as a whole. This paper asks whether miners are individually incentivized to propagate blocks, one of the most fundamental processes in Bitcoin. Miners collectively maintain the blockchain by generating blocks and disseminating them across the network. If miners have an incentive not to propagate some blocks, this would indicate a fundamental flaw in Bitcoin's incentive design. Although prior work has studied how propagation delays affect forks and mining rewards, it has not fully characterized miners' incentives to improve block propagation under different tie-breaking rules. To address this gap, we derive analytical reward expressions for each tie-breaking rule based on a blockchain network model that captures the effect of forks on mining fairness. These expressions explicitly characterize how block propagation delays, hashrate distribution, and tie-breaking rules jointly determine mining rewards. We then use them to analyze miners' incentives to improve block propagation. Our results show, for example, that miners have no mining-reward incentive to relay blocks generated by other miners. By contrast, under the first-seen rule, every non-majority miner is incentivized to receive other miners' blocks more quickly and to propagate its own blocks more quickly. Finally, we compare tie-breaking rules and identify a trade-off between propagation incentives and mining fairness. In particular, the first-seen rule provides the strongest incentives to reduce propagation delays, but it also worsens mining fairness the most.
Decentralized Finance (DeFi) services are usually constructed by composing a variety of smart contracts. While composability is a key driver of the success of DeFi, it also creates security risks: adversaries may exploit interactions between newly deployed contracts and the pre-existing ones to inflict economic losses. We introduce MEV non-interference, a formal security notion for DeFi composability requiring that the maximal extractable value from a set of newly deployed contracts is not increased by interactions with the existing blockchain state. To support this notion, we define local MEV, a novel measure of economic attacks that focusses on the loss of a given set of victim contracts. We study two adversarial models, with bounded and unbounded wealth, and establish sufficient conditions and locality principles that enable modular reasoning about secure composability. We apply the framework to representative DeFi compositions, including exchanges, AMMs, options, lending pools, routers, and arbitrage contracts, showing how it distinguishes secure compositions from vulnerable ones. Our results provide a formal foundation for reasoning about the economic security of DeFi compositions.
We present Veil, a decentralized messaging protocol that unifies metadata protection, spam prevention, and offline message delivery through a single mechanism: Proof-of-Relay. In Veil, sending a message requires a zero-knowledge proof that the sender has faithfully relayed messages for others through a stratified mixnet. The relay work itself constitutes the anonymizing infrastructure, eliminating the need for cryptocurrency tokens, blockchain consensus, or trusted third parties. We make three contributions. First, we prove that bilateral non-transferable credits with epoch-bound nullifiers achieve incentive compatibility without a global state, a general result applicable beyond messaging to any peer-to-peer system requiring fair exchange. Second, we establish a Growth-Isolation Impossibility theorem showing that no CRDT merge function can simultaneously resist inflation and guarantee completeness for monotonically growing verifiable evidence, and present a resolution via penalty-log CRDTs with locally-computed growth. Third, we prove a constructive adversary bound: any adversary controlling a fraction f of relay nodes necessarily contributes to sender anonymity entropy, while the individual deanonymization probability remains bounded, ensuring that adversarial participation requires a productive contribution while individual targeting remains negligible. Veil requires no economic investment to participate; privacy is earned through device contribution alone. We analyze the protocol's security under a global passive adversary with formal indistinguishability definitions, bound Sybil infiltration under depth-limited social vouching, and demonstrate mobile feasibility with verified constraint counts via Nova folding over BabyJubjub.
Decentralized verifiable credential systems have seen limited deployment in practice. Existing constructions, built on zero-knowledge proofs, are complex, application-specific, and largely restricted to predicates over structured data. We present Privately Inferred Credentials ($Ï€$Creds): privacy-preserving, legacy-compatible, decentralized verifiable credentials generated by trusted LLM inference over authenticated data. LLMs' ability to semantically reason over unstructured data substantially expands the range of claims $Ï€$Creds can certify over existing credential systems. The use of LLMs also introduces new application-level threats, which we formalize through two problems: the Source-Constrained Adversarial Example (SCAE) problem, which captures robustness against adversaries that manipulate authenticated data to obtain misleading credentials, and the Authenticated Covert Predicate Poisoning (ACPP) problem, which captures privacy leakage through adversarial model selection. We characterize applications of $Ï€$Creds over user data, and a novel class of credentials over proprietary software that certifies properties of a service without revealing its source code. Our prototype supports issuing credentials over live financial, health, email, and code sources, and we empirically study the SCAE and ACPP threats on a product expertise credential over real financial data.
Abstract Ethereum's transaction validity model is currently anchored in ECDSA over secp256k1, whose security assumptions weaken in the presence of large-scale quantum adversaries. While NIST-standardized post-quantum signature schemes such as ML-DSA, SLH-DSA, and FALCON provide resistance against quantum attacks, integrating these schemes into Ethereum introduces significant systems-level challenges involving bounded execution, gas determinism, and adversarial verification complexity. This paper introduces PQSigAbstract, a modular post-quantum signature verification architecture for Ethereum that separates validation into a stateless pre-validation phase and a deferred cryptographic verification phase linked through commitment binding. The design defines typed Verification Modules with explicit gas estimation, a versioned Scheme Registry with quarantine-based deployment safety, and a probabilistic aggregation mechanism for non-aggregatable post-quantum schemes. The proposed architecture preserves EU-CMA security while maintaining compatibility with ERC-4337 and RIP-7560 account abstraction models. Formal gas cost models are derived for ML-DSA-44, FALCON-512, and SLH-DSA-128f, and empirical evaluation demonstrates practical deployment feasibility for high-value Ethereum accounts despite substantially higher verification costs relative to ECDSA. Status: Technical Report / Working Paper Author: Ankita Virani Affiliation: University of Colorado Boulder
Adiwena Putra, Cuong Manh Duong, Anh Quang Pham, Joo-Young Kim
Zero-knowledge proofs (ZKP) allows a prover to convince a verifier of computational correctness without revealing private data, ensuring both privacy and verifiability. However, proof generation is highly compute-intensive, dominated by polynomial (POLY) and elliptic-curve (EC) operations. These workloads pose two key challenges for hardware acceleration: (1) efficiently supporting diverse large-precision modular multiplications, and (2) maintaining high utilization across workloads that dynamically shift between POLY and EC stages. Existing reconfigurable accelerators address these issues only partially, remaining limited in precision scalability, algorithmic flexibility, and resource efficiency. To overcome these limitations, we propose ZK-Flex, a flexible and scalable software-hardware co-designed framework for accelerating ZKP proof generation. The software layer incorporates POLY and EC optimizers that reduce computation through hardware- and workload-aware algorithmic choices, while the hardware integrates TCore, a Toom-Cook-based multi-precision core with a flexible NoC and a linked-list memory mechanism that improves parallelism under limited memory capacity. Across representative ZKP benchmarks, ZK-Flex achieves 5 to 11 times speedup and up to 3.8 times higher area efficiency over the state of the art, establishing a new foundation for high-performance, reconfigurable ZKP acceleration.
Many proof-of-stake protocols finance validator rewards from two sources: transaction fees and a finite reserve of tokens. This creates a dynamic hand-off problem. Early in the life of the system, fees may be too small to fund the target level of security; later, fees may become sufficient. The central question is whether the reserve provides enough runway for the protocol to remain secure until this fee-only region is reached. We study this problem in a discrete-time stochastic model of validator participation. Token price and transaction demand fluctuate over time, while validators choose participation strategically. We solve the validator entry game and derive an exact state-dependent reserve threshold, i.e., the minimal reserve stock necessary and sufficient to sustain a target security level. This threshold separates three regions: infeasibility, reserve-dependent security, and fee-only security. Security fails if the reserve first falls below the state-dependent threshold, and a successful hand-off occurs exactly if the fee-only region is reached before that failure time. We derive stress-test guarantees that convert lower confidence bands for token price and demand into reserve requirements, and obtain explicit failure-probability and expected hand-off-time bounds. Finally, we extend the model to forward-looking validators and derive the Markov participation condition that captures how current participation affects future reserve-funded rewards. The main implication is that reserve policy should not be evaluated by nominal depletion dates or steady-state reward ratios alone. A protocol can have a large nominal reserve and still be close to security failure after adverse price or demand shocks. Conversely, once demand crosses the fee-only threshold, the reserve becomes redundant for security. This paper provides a tractable equilibrium framework for stress-testing this transition.
Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.
Jyotiplaban Talukdar, Ferdous Ahmed Barbhuiya, Shouraya Mishra, Shubhashish Shukla
Asset ownership tracking is fundamental to economic activity, yet it remains fragmented across paper-based deeds, centralised registries, and siloed databases. Public blockchains provide immutability but suffer from scalability limitations and inflexible consensus mechanisms unsuitable for enterprise deployment. We present Keychain Signet (KCS), a decentralised architecture designed as a notarised asset ledger that strictly separates application logic, consensus, and persistence. KCS distributes responsibilities: service providers manage application workflows, notaries enforce validity and uniqueness via BLS signatures, and storage nodes execute BFT-Raft consensus to order blocks. This architecture enforces a "notarised append" model where authorised writes are persisted and ordered by the storage cluster. Unlike standard blockchains, our architecture resolves the latest asset state in O(1) time via a disc-backed Distributed Hash Table while maintaining an immutable, cryptographically linked history for auditability. Experimental evaluation demonstrates 401.1 TPS throughput, notary signing latency of 1.6–10.2 ms scaling linearly with committee size, signature verification under 0.81 ms, and storage persistence under 700μs. The system tolerates Byzantine faults, storage corruption, and node crashes while preserving safety and liveness guarantees.