Current approaches to verifying AI training data compliance face a fundamental tension: copyright holders need to know whether their content was used in training (EU AI Act, Article 53(1)(d)), while model providers need to protect their training data as trade secrets (GDPR, trade secret law). Existing zero-knowledge proof systems for machine learning (ZKML) address this partially by providing proofs of non-membership for exact data points. However, real-world training pipelines involve tokenization, chunking, paraphrasing, and augmentation, rendering exact-match proofs insufficient. We identify a gap in the literature: no existing system combines semantic fingerprinting with zero-knowledge proofs to enable semantic non-membership verification. We propose an architecture for Zero-Knowledge Semantic Non-Membership (ZK-SNM) that enables a model provider to prove, without revealing any training data, that no document in their training corpus is semantically similar to a queried document above a specified threshold. We discuss the technical challenges, including the computational cost of similarity search within ZK circuits, and propose mitigation strategies based on locality-sensitive hashing and hierarchical verification. This position paper establishes the problem formulation and proposed architecture; experimental validation is left to subsequent work.
The growing frequency of malicious attacks on Internet of Things (IoT) devices has rendered conventional approaches with static label-dependent risk assessment models obsolete, especially when coping with unknown and continuo... | Find, read and cite all the research you need on Tech Science Press
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GLYPH is a transparent verification layer for Ethereum for trustless on-chain verification of heterogeneous proof systems. It unifies upstream SNARK and STARK settlement through a single packed arity-8 sumcheck verifier over p = 2^128 - 159, while preserving upstream assumptions. The design centers on a universal adapter surface, UCIR compilation, and a chain-bound artifact interface for stateless verification. Benchmark evidence in the whitepaper reports 29.45k total transaction gas in recorded testnet receipts. This record includes the whitepaper and the formal proof appendix.
Ensuring transparency, security, and privacy in agricultural food supply chains is critical for maintaining consumer trust, regulatory compliance, and data integrity. Traditional centralized traceability systems suffer from several limitations, including data tampering risks, single-point failures, and potential privacy leakage. To address these challenges, this research proposes a privacy-preserving blockchain-based traceability framework that integrates the InterPlanetary File System (IPFS) with Zero-Knowledge Proofs (ZKPs). The framework leverages the Ethereum blockchain for immutable record-keeping, while zk-SNARK-based proofs enable compliance verification without revealing sensitive underlying data. A prototype was implemented using Solidity smart contracts and Python-based zk-SNARK circuits. Experimental evaluation across varying record sizes, from 50 to 200, demonstrates high security and efficiency, achieving 100% success in detecting simulated tampering attempts. Performance metrics indicate a highly scalable system with an average end-to-end latency of approximately 0.33 seconds, rapid proof generation times of approximately 0.0002 seconds, and near-constant verification times averaging 0.027 seconds. Furthermore, the system maintains a consistent simulated transaction cost of 20.40$ per proof, regardless of the total records processed. Overall, the proposed approach provides a robust, scalable, and computationally efficient solution for modern agri- food supply chains, successfully balancing data confidentiality with rigorous cryptographic integrity.
Open access
Food Supply Chain Traceability
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Digital systems increasingly rely on user location data, raising significant privacy concerns. This study proposes a privacy-preserving location data utilization system that eliminates the need for dedicated base stations by integrating blockchain technology with zero-knowledge proof scheme. Our system converts data from smartphone trajectory data into zero-knowledge proof values and records only these proof values on the blockchain. Thus, the system enables verification of user movement without revealing sensitive information. By integrating the entire process with smart contracts on the blockchain, our system automates transaction processing and monetary transfers without relying on any specific organization. We conduct an experimental evaluation on the blockchain using trajectory data collected from a smartphone application.
Pankaj Kumar, Arun K H, Yogesh N, Prakash Babu · 8 authors
The growing dependance on data-based decisionmaking in healthcare has brought attention to the vital importance of secure, privacy-preserving and collaborative learning techniques. Traditional centralized learning approaches in medical data often raise concerns regarding patient privacy data leaks and even regulatory troubles. Federated learning came as a good solution, where it allows model training in different hospitals without sharing the sensitive patient data. However, federated learning has its problems - it can be mislead with fake updates, the model can even be poisoned and it is really hard to trust every participants involved. In this work, we present fed-chain, a secure and scalable framework which brings together federated learning, blockchain and zero-knowledge-proofs(ZKPs) preserving the privacy of patient's data in healthcare. Blockchain here adds decentralized trust, immutability and makes model updates transparent to review while ZKPs helps in proving correctness without leaking personal data. We are implemented this framework for heart disease prediction where multiple hospitals train the model together but the data stays confidential. Our experimental results shown better accuracy, more strength against attacks and even low communication cost compared to other FL setups. Overall, the systems gives a safer approach for working together on healthcare data, allowing hospitals and research centers to generate valuable predictions using these models while keeping the patient data private and safe.
Airdrops are widely used in blockchain ecosystems as mechanisms for token distribution, user bootstrapping, and governance decentralization. These mechanisms frequently rely on balance or stake snapshots taken at specific blockchain heights or epochs to determine eligibility. However, snapshot-based distribution introduces a critical temporal vulnerability: the assumption that momentary state accurately represents sustained economic participation.This paper defines and analyzes <b><i>Airdrop Snapshot Spoofing</i></b>, a class of temporal state manipulation attacks in which adversaries exploit the gap between snapshot definition, execution, and settlement to illegitimately capture token allocations. We demonstrate that such exploits are not edge cases but structural weaknesses inherent to snapshot-based systems across Proof-of-Stake (PoS) and tokenized networks. We further argue that snapshot spoofing represents a core-layer economic security failure rather than a marketing or distribution flaw, and we outline mitigation strategies based on time-weighted enforcement and validator-level continuity checks.
<b><i>Zombie Validator Resurrection</i></b> is a core consensus-layer exploit in Proof-of-Stake (PoS) and alternative Layer-1 networks where inactive, slashed, or economically abandoned validators regain influence without restoring proportional economic security. Through protocol gaps, state resets, or weak liveness enforcement, validators that should be neutralized re?-enter consensus, undermining safety assumptions and enabling stealth attacks. This paper formalizes the structural conditions enabling zombie validators, analyzes common resurrection mechanisms, and examines systemic risks to consensus integrity. We propose mitigation strategies to enforce validator lifecycle accountability and safeguard decentralized networks against stealth reactivation attacks.
Distributed ledger technologies rely heavily on consensus mechanisms to maintain a synchronized, tamper-resistant, and decentralized state across a network of mutually untrusted nodes. Conventionally, analyses of these mechanisms concentrate on cryptographic security, equilibrium in game theory, and network latency but often consider system dynamics to be linear predictable or stationary. This paper applies chaos theory to provide an integrated complex systems framework for the nonlinear, dynamic behaviors of three classical blockchain consensus paradigms: Proof of Work (PoW), Proof-of-Stake (PoS), and Byzantine Fault Tolerance (BFT). Through nonlinear feedback loops modeling transaction flows, validator behaviors, and fork-generation processes under the right boundary conditions local computational or stake centralization, sudden network propagation delays, and targeted malicious adversarial perturbations- we prove that deterministic chaos is self-generating. Employing state-space reconstructions, sensitivity analyses to initial conditions, and qualitative descriptions of phase trajectories, this work charts the transition between stable decentralized consensus phases as echoed through chaotic divergence or quasi-permanent chain splits. Results identify major flaws in classical protocols and provide principles to design the next-generation robust chaos-tolerant distributed architectures.
The finite 1-bullet silent duel is considered, involving two duelists who shoot with exponentially-convex accuracy through a uniformly quantized time. The duel is a symmetric matrix game whose optimal value is 0, and each of the duelists has the same optimal behavior, whether it is in pure or mixed strategies. The actual beginning is never optimal in the duel. Apart from the very end of the duel, the conditions for the optimal time moment existence are found. Numerical experiments confirm that the optimality can be manipulated by changing the accuracy factor that scales the payoffs. The results are applicable in systems under limited or censored communication with uncertainty, latency, and lucrative delayed actions. Some examples of such set-ups are time-sensitive information release (privacy and censorship), queueing and load balancing (information science and telecommunication systems), and block proposal timing for decentralized consensus protocols (in Proof-of-Work and Proof-of-Stake).
Mohammad Fairus Bin Zulkifli, Rabiah Abdul Kadir, mohamad nazir ahmad
Growing reliance on digital knowledge sharing across academic, corporate, and public sectors has raised serious concerns about data integrity, trust, and security. Blockchain consensus mecha-nisms offer a promising path forward through decentralized, transparent, and tamper-proof frameworks. This systematic review examines how these mechanisms enhance trust in knowledge sharing platforms, focusing on four directions: how these mechanisms are applied within knowledge sharing con-texts, the challenges they introduce for knowledge sharing de-ployment, and the advantages they provide to trust-based knowledge sharing ecosystems. Following PRISMA 2020 guide-lines, three databases Scopus, IEEE Xplore, and Web of Science were searched, and peer-reviewed studies published between 2020 and 2025 were selected for analysis. In terms of knowledge sharing applications, blockchain consensus mechanisms build trust through multiple co-occurring pathways, including distrib-uted verification, transparency, cryptographic security, immu-tability, incentive alignment, and smart contract automation. Algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, and Byzantine Fault Tolerance variants are widely adopted, each offering different trade-offs between secu-rity, efficiency, and scalability. In terms of challenges, scalabil-ity, energy consumption, and integration complexity with exist-ing systems remain the most significant barriers to adoption. In terms of advantages, blockchain consistently delivers stronger data security, greater transparency, and reduced dependence on centralized authorities across knowledge sharing contexts. This review concludes that blockchain consensus mechanisms offer layered and compounding trust benefits, yet technical and or-ganizational barriers continue to limit widespread deployment. Future research should focus on energy-efficient protocols, scalable architectures, and real-world effectiveness studies.
When Ethereum (ETH) shifted from a Proof of Work (PoW) protocol to a Proof of Stake (PoS) protocol, not all users were enthused. We use Ethereum’s shift from PoW to PoS as a case study for the broader question of whether developers of a blockchain owe its members certain fiduciary or fiduciary-like duties. We argue that if done properly, in accordance to the rules governing the blockchain, then developers do not necessarily owe fiduciary responsibility to other members of the chain, but they nonetheless may owe fiduciary-like responsibilities to users inadvertently and negatively impacted. We argue these users may be entitled to an oppression claim akin to what minority shareholders may be entitled to in the corporate law context.
<b><i>Governance Fork Farming</i></b> is a strategic exploitation pattern in decentralized finance (DeFi) and proof-of-stake (PoS) ecosystems where actors repeatedly engineer, anticipate, or provoke governance forks to extract economic rewards. By positioning capital, validator power, or voting rights ahead of contentious governance events, attackers harvest duplicated assets, incentives, or control advantages across forked states. This threat undermines governance legitimacy and destabilizes network continuity without violating protocol rules.
In 2025, the largest cryptocurrency heist in history cost Bybit $1.5 billion because of a flaw known as blind signing. Essentially, cryptocurrency wallets ask users to sign transactions that look like gibberish code, so users often approve them based on trust and habit rather than real understanding. Attackers can exploit this by changing transaction details behind the scenes while keeping the on-screen transaction looking correct. In my project, I tackle this vulnerability in an Ethereum multi-signature wallet setting where multiple people must approve the same transaction. I first build a web-based simulation of the Bybit attack to understand how transaction swapping can mislead signers. Next, I implement four safety features across the transaction lifecycle: (1) Metadata validation to show the actual details of the transaction that is being signed, (2) Transaction simulation to preview what will happen to the funds, (3) Backend integrity verification to ensure the signed transaction has not been altered, and (4) Address whitelisting of approved destination addresses. My evaluation results show that these defences add negligible computational overhead, but they increase the time a user spends reviewing and confirming a transaction. To balance security with day-to-day usability, I propose an adaptive security approach that uses all defences for large, infrequent transactions, and a lightweight defence model for small, routine payments.