Rodica Doina Zmaranda, Attila-Imre Kovacs, Daniela Elena Popescu, Alexandrina Mirela Pater
Securing electronic health records (EHR) requires strong guarantees for confidentiality, integrity, access control, and auditability. Traditional centralized architectures rely on database-level protection and internal logging, which remain vulnerable to insider misuse and undetected data modification. This study proposes a practical hybrid architecture in which medical content is stored encrypted off-chain, while blockchain is used selectively as a governance and evidence layer. An Ethereum-based prototype was designed and implemented to support integrity anchoring of medical documents, patient-controlled consent management, and immutable audit trails for critical actions. In the implemented solution, the actual medical content is not stored on-chain. Instead, the blockchain stores only document-related metadata, cryptographic hashes, document references, and access-control information, while the sensitive medical data remains encrypted and stored off-chain. This design supports GDPR-oriented data minimization, since the immutable blockchain layer does not contain raw medical records or directly identifiable medical content. The prototype separates confidentiality from blockchain immutability. Medical document confidentiality is handled at the application and off-chain storage level, while the blockchain is used for integrity verification, consent management, and auditability. Encryption keys are not stored on-chain, which prevents the blockchain layer from becoming a repository of sensitive or directly exploitable medical information. Security mechanisms are integrated directly into application flows, including hash-based tamper detection and on-chain verification of access rights. The prototype is evaluated through realistic operational scenarios, analyzing security properties, performance, and transaction cost implications. Results show that, relative to a DB-only baseline, the hybrid approach provides structurally stronger support for integrity verification, traceability, and accountability without exposing sensitive medical data on-chain. The study also highlights practical limitations related to latency and costs in public blockchain environments, supporting a selective on-chain design focused on high-value operations.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Michael Adjedj, Constantin Blokh, Geoffroy Couteau, Arik Galansky ¡ 6 authors
We present a novel protocol for two-party ECDSA that achieves two rounds (a single back-and-forth communication) at the cost of a single oblivious linear function evaluation (OLE). In comparison, the previous work of Boneh, Haitner, Lindell, and Segev (EUROCRYPT 2025) achieves two rounds but requires expensive zero-knowledge proofs on top of the OLE. We demonstrate this by proving that in the generic group model, any adversary capable of generating forgeries for our protocol can be transformed into an adversary that finds preimages for the ECDSA message digest function (e.g., the SHA family). Interestingly, our analysis is closely related to, and has ramifications for, the âpresignaturesâ mode of operationâCanetti, Gennaro, Goldfeder, Makriyannis, and Peled (CCS 2020), Groth and Shoup (EUROCRYPT 2022).Motivated by applications to embedded cryptocurrency wallets, where a single server maintains distinct, shared public keys with separate clients (i.e., a star-shaped topology), and with the goal of minimizing communication, we instantiate our protocol using Paillier encryption and suitable zero-knowledge proofs. To reduce computational overhead, we thoroughly optimize all components of our protocol under sound cryptographic assumptions, specifically small-exponent variants of RSA-style assumptions.Finally, we implement our protocol and provide benchmarks. At the 128-bit security level, the signing phase requires approximately 50 ms of computation time on a standard linux machine, and 2 KB of bandwidth.
Ioannis Tzannetos, Danai Balla, Aris Pagourtzis, Vassilios Vescoukis
Non-fungible tokens (NFTs) have created vibrant digital marketplaces where unique assets are exchanged across domains such as art, gaming, and music. While current infrastructures are optimized for pairwise, currency-backed trades, they provide limited support for multi-party swaps of indivisible assets based on user preferences. In practice, liquidity is not always desirableâparticipants may wish to exchange directly for assets they deem equally valuable, bypassing auctions or currency markets. In this paper, we propose BarterSwap, a protocol to address this gap by leveraging the Top Trading Cycles (TTC) algorithm to enable efficient multi-party NFT exchanges on Ethereum. Our protocol identifies preference-based dependencies among users and executes swaps without requiring external liquidity. We implement and deploy our solution on the Ethereum blockchain, demonstrating that it remains practical for a reasonably large number of participants. Finally, we release our implementation publicly and provide a detailed cost analysis, offering a concrete path toward fair and efficient preference-based NFT exchanges.
The advent of sufficiently powerful quantum computers poses an existential cryptographic threat to elliptic-curve-based public key infrastructure, upon which major blockchain networks depend for transaction security and identity. This paper conducts a rigorous comparative analysis of quantum risk exposure for Bitcoin and Ethereum, examining the structural, governance, and economic dimensions of post-quantum cryptographic (PQC) transition for each protocol. We analyze the mathematical incompatibility of leading NIST standardized PQC signature schemes with current blockchain scalability constraints, with particular attention to signature size inflation (30-100Ă current schemes), the loss of algebraic linearity preventing signature aggregation, and the resulting implications for block space, fee markets, node economics, and validator infrastructure. We subsequently contrast Ethereum's upgrade-oriented, stake-weighted governance model and its modular cryptographic architecture against Bitcoin's deliberately ossified, consensus-driven governance structure. Our findings indicate that while Ethereum possesses the structural and institutional prerequisites for a credible, phased transition to post-quantum cryptography, Bitcoin's governance model and architectural constraints render such a transition highly contested and potentially irresolvable without chain fragmentation. We conclude that Bitcoin's structural limitations, compounded by deep ideological fractures and the irreversible nature of PQC deployment, place it at significant risk of prolonged governance stagnation or chain split, undermining its position as a reliable store of value and 'digital gold' standard in the medium term.
Rajasekaran P., Duraipandian M., Johny Renoald Albert, R. Jamuna ¡ 5 authors
The Internet of Medical Things (IoMT) in the IoT with Cloud Healthcare (CHI) creates a high volume of realâtime medical data, but traditional compression methods suffer high computation costs, privacy leaks and quantum attacks, while advanced cryptographic algorithms such as homomorphic encryption are costly and have poor scalability for the realâtime system application. In this work, we propose a quantumâenhanced zeroâknowledge healthcare compression network (QZâHCN) that associates zeroâknowledge proofs (ZKPs) with quantumâinspired deep learning (QIDL) by introducing an innovative adaptive quantumâsupported ZKP verification mechanism (AQâZKV) and a quantum fusion autoconventional neural network (QFâAutoCNN) technique to achieve efficient, privacyâpreserving compression. For healthcare IoT datasets, QZâHCN can reach 98.16% in accuracy, 97.09% in Fâmeasure, 96.32% in precision and 97.45% in recall, with a throughput of 449.57 bits/s; processing time is reduced to 0.85 s, and memory cost is minimised to be only 192 kbits, which outperforms CNNâEncryption (90.23% accuracy), proxy reâencryption and homomorphic encryption by at most 13 percentage points in accuracy and 75 percentage points in memory efficiency. The secure and scalable management for CHI data is achieved by QZâHCN, which solves the problems of privacy threats and space costs of realâtime medical applications.
Federated Learning (FL) enables collaborative training without centralizing sensitive data but faces challenges, including client authenticity, verifiable training participation, and secure aggregation. To overcome these challenges, we propose a novel framework, Zero-Knowledge Reputation-aware Blockchain Federated Learning (ZK-RBFL), which integrates blockchain, FL, Homomorphic Encryption (HE), and zero-knowledge proofs (ZKP). In the proposed ZK-RBFL framework, initially the clients undergo lightweight token-based authentication and then generate ZKP to provide cryptographic evidence of honest local training participation and reported inference accuracy before contributing their model updates. The model updates are encrypted using the CKKS HE mechanism to prevent any potential model inversion attacks. These encrypted model updates are stored on IPFS, with their corresponding CIDs recorded on the blockchain to ensure immutability. Further, ZK-RBFL enables mutual client verification of ZKPs to reduce server bottlenecks and enhance accountability. To ensure fairness and robustness in a distributed environment, we introduce a democratic blockchain consensus mechanism named Proof of Reputation-Weighted Voting (PoRWV) for block acceptance. Once consensus is reached, the encrypted model updates are aggregated using reputation-weighted averaging. We demonstrate the effectiveness of ZK-RBFL for brain tumor classification using a ZKP-compatible LeNet model for proof generation. Despite model simplicity, the global model achieves 94.22% accuracy. In addition, experiments with malicious clients and formal Scyther security analysis demonstrate that ZK-RBFL ensures both security and performance.
Biometric authentication provides high convenience with the drawback of privacy leakage, replay attacks, and centralized control over biometric templates. This paper introduces an Ethereum-based decentralized biometric authentication framework that uses Elliptic Curve Digital Signature Algorithm (ECDSA), InterPlanetary File System (IPFS) storage, and an on-chain challengeâresponse protocol. In the proposed model, encrypted biometric templates are stored of-chain in IPFS, whereas their content identifiers (CIDs) are registered in Ethereum smart contracts. Every authentication attempt necessitates a new on-chain nonce and an ECDSA signature of the concatenation of the CID and the nonce, authenticated through Ethereumâs built-in method, ecrecover. The design supports explicit replay protection, revocation, and public auditability. Deployment of the prototype on Ganache and MetaMask reveals that the scheme provides secure, transparent, and tamper-proof authentication with minimal gas consumption on FVC2004 datasets and reasonable storage usage on Ethereum.
Open access
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Robiah Arifin, Wan Azelee Wan Abu Bakar, Mustafa Man, Mohamad Afendee Mohamed ¡ 5 authors
The issue of fake certificates has been widely identified, and their prevalence has increased significantly in recent years. This growing trend has become a global concern due to its adverse impact on educational standards. A key factor contributing to the problem is the continued reliance on manual processes for issuing and verifying certificates. To address these challenges, this study proposes the use of an authority round (AuRa) consensus algorithm for managing certificate data on the Ethereum blockchain. AuRa, a member of the proof of authority (PoA) family, facilitates consensus among nodes distributed across multiple servers and networks. This mechanism plays a vital role in preserving the integrity and decentralization of the blockchain while ensuring the security of transactional data. Furthermore, the study investigates how AuRa enables efficient certificate data transactions within a private Ethereum environment. It also evaluates the algorithm's performance in terms of transaction speed per second (TPS) and throughput per second (TGS), demonstrating its effectiveness for managing certificate transactions on a blockchain network. Then the TPS and TGS results substantiate the suitability of AuRa for digital certificate generation, evidenced by its stable and efficient performance within a controlled private server environment.
The exponential growth of connected devices and embodied intelligent systems in B5G and 6G networks demands secure, adaptive, and autonomous communication among distributed agents. However, ensuring privacy-preserving coordination among these Agentic AI systems remains a major challenge, particularly in decentralized environments where transparency conflicts with confidentiality. To address this issue, we propose a Smart-contract-based Embodied Covert Agent Communication Architecture (SECA), which integrates Ethereum election contracts with image steganography to enable covert, high-bandwidth communication among intelligent agents. In this framework, Blockchain-based agents utilize candidate images as visual carriers to embed encrypted messages, achieving imperceptible data exchange during on-chain interactions. We further design a Stackelberg Minimal Control Algorithm (SMCA) that enables adaptive manipulation of voting agents to ensure communication reliability with minimal control cost. Experimental results demonstrate that our approach achieves a transmission bandwidth up to 103Ă higher than ORIM-based covert channels and passes multiple detection benchmarks (K-S and Ď2tests), all without incurring additional gas costs. This work provides a foundational perspective for secure Agentic AI communication frameworks, bridging embodied intelligence, decentralized networking, and covert information transmission in emerging 6G environments.
Advanced Steganography and Watermarking Techniques
This paper introduces a secure state-sharding solution for permissionless blockchains that supports both payment transactions and general Ethereum-like smart contracts. The secure partitioning of the blockchain into distinct shards continually adapts to the current transaction workload, encompassing both user and smart contract transactions. A novel two-phase commit protocol guarantees the correct execution of smart contracts distributed across different shards. Notably, this protocol offers, for the first time, support for smart contracts with nested calls across shards, without any limitation on the depth of the calling graph. An experimental study highlights the benefits of the adaptive behavior of shards during the distributed execution of such nested smart contracts.
Bitcoin's block reward is scheduled to decline to zero, raising concerns about whether the network can remain secure once miners rely solely on transaction fees. This paper seeks to identify the conditions under which large-scale and persistent deviation from honest mining can arise. We analyze and compare the payoffs of honest and deviating miners in a sequential decision model, and identify a deviation threshold $G_t$ at which honest mining ceases to be privately optimal. Around the 2024 Bitcoin halving, we show that current mining behavior does not exhibit large-scale or structural deviation. However, when the block reward is removed, the $G_t$ criterion implies that deviation can arise even with a very small fraction of transaction fees. Finally, we evaluate three protocol-level mechanisms: Base Fee, Fee Floor, and an adaptive maximum block size rule, and show that their combination raises the deviation threshold and mitigates incentive breakdown in a fee-only regime. These results provide a practical benchmark for assessing Bitcoin's security as block rewards disappear.
Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a novel protocol that enables native Bitcoin to access DeFi through trust-minimized infrastructure while maintaining self-custody of funds. BSA achieves this through a combination of emulated Bitcoin covenants using Partially Signed Bitcoin Transactions (PSBTs) and Taproot scripts, a Trusted Execution Environment (TEE)-based arbitration system, and destination chain smart contracts that enable DeFi platforms to accept self-custodial Bitcoin as collateral without necessitating protocol-level modifications. The setup leverages liquidity secured by the Lombard Security Consortium which provides a twofold advantage: for a DeFi protocol, liquidators rely on fungible assets with deep liquidity to quickly exit positions, while for a depositor, the general trust assumptions of honest majority (m-of-n) are reduced to existential honesty (1-of-k). We present the complete protocol design, including the Bitcoin architecture, the TEE-based arbitration mechanism, and the Smart Account Registry for protocol management. We provide a security analysis that demonstrates the correctness, safety, and availability properties under our trust model. Our design enables native Bitcoin to serve as collateral in lending markets and other DeFi protocols without requiring users to relinquish custody of funds.
Smart transportation networks have the potential to significantly improve traffic flow. The Internet of Vehicles (IoV) serves as a vital component of such networks, enabling real-time connectivity and coordination among vehicles and infrastructure. The expansion of IoV-based communication and the increasing volume of data transferred across the IoV make it necessary to implement effective techniques for preserving privacy and ensuring information security. Nonetheless, traditional data-security models have notable drawbacks, primarily high computational costs. In our pseudonymous authentication framework, each vehicle first generates a publicâprivate key pair using a multidimensional lattice-based (Nth-degree truncated polynomial ring units) method. A vehicle then digitally signs its own identity with its private key and sends an authentication request to the roadside unit (RSU); the RSU then verifies that signature using the public key of the corresponding vehicle. After verification, the vehicle and RSU execute a secure ephemeral-key agreement using ephemeral supersingular isogeny DiffieâHellman to establish a shared session key. The complete authentication and session-key-agreement process is securely signed and documented on the blockchain using a lightweight enhanced delegated proof-of-stake consensus methodology to efficiently confirm the transaction and add it to the blockchain. The experimental findings show that the proposed system incurs a computational cost of 12.1 ms and a communication cost of 1184 bits. Furthermore, smart contracts are deployed on the Remix virtual machine to showcase the functionality of the proposed system within a decentralized blockchain environment. The smart contract execution costs are (681,713), (734,851), and (870,301) for the RSU registry, vehicle registry, and session AuthTrust, respectively. The proposed scheme is comparatively evaluated against existing frameworks, namely PBSCF-ITS, AAKE-BIVT, IIoT-QRSCA, and BASF-ITS, using several metrics, including both computational and communication costs. The effectiveness and security of the proposed model are also verified through a security analysis. The results confirm that the proposed system outperforms similar existing baseline models.
Blockchain technology has transformed distributed systems by enabling mutually untrusted nodes to reach agreement without a central authority. Such trustless decentralized paradigm relies on the robustness of system design mainly from two components: the consensus layer governing block production and the data layer governing data consumption. However, these pillars of robustness could be undermined in a Byzantine environment, where adversarial participants may strategically misbehave, leading to biased data production and compromised data access. This thesis systematically addresses robustness vulnerabilities across both layers, ensuring that blockchain systems remain fair, predictable, and verifiable throughout the entire data lifecycle. At the consensus layer, we first address selfish mining in Proof-of-Work (PoW), which allows adversarial miners to gain disproportionate revenue. We introduce an unfairness metric based on the divergence between computing power and mining revenue, and propose Tit-for-Tat (TFT), a block-promotion strategy that detects suspicious forking behavior and selectively delays block propagation. To optimize this defense, we formulate the Delay Vector problem and develops efficient approximation algorithms. Second, we study block withholding in Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS), where proposers may delay blocks to extract additional Maximal Extractable Value (MEV). To restore predictable block generation, we propose InTime, an incentive mechanism that rewards timely proposals according to transaction arrival rates observed across the network. InTime combines an Arrival Rate Incentive, a Committee Time Witness workflow, and a Shift-Mean Estimation algorithm to collect and verify timing information under adversarial conditions. At the data layer, we propose the Merkle Bloom Filter Tree (MBFT), a framework for authenticated aggregate queries with keyword and range predicates. MBFT supports efficient verification for complex on-chain queries, including time-window queries, while controlling storage overhead. We design a novel Merge Bloom Filter (MBF) for space-efficient handling of dynamic sets during query authentication.
The SPT-Txn Framework v6.0 integrates Attribute-Based Access Control (ABAC), Token-Based Access Control (TBAC), Non-Fungible Tokens (NFTs), Self-Sovereign Identity (SSI), Zero-Knowledge Decentralized Identifiers (zkDID), biometric uniqueness proofs, and IETF Transaction Tokens into a unified authorization architecture for the agentic economy. This working paper is developed in conjunction with IETF Internet-Draft draft-coetzee-oauth-spt-txn-tokens-00, available at https://datatracker.ietf.org/doc/draft-coetzee-oauth-spt-txn-tokens/
Secure Aggregation (SA) is a fundamental privacy-preserving technique in Federated Learning (FL) that ensures the confidentiality of local model updates while enabling global model aggregation. Previous studies have implemented SA within the FL architecture that includes a central server. However, in a Device-to-Device (D2D) based FL, decentralized SA becomes challenging due to the lack of a central server, particularly in a zero-trust network vulnerable to Byzantine attacks. To address this issue, we present a novel Byzantine-robust decentralized SA protocol (DeSA) that guarantees the integrity of model training and aggregation while protecting the privacy of model updates. Specifically, we utilize an enhanced zk-SNARK proof system to verify the local model training process. Additionally, we propose a framework that embeds multiple zero-knowledge proofs to ensure the integrity of model aggregation, while maintaining succinct proofs and fast verification. Moreover, we present a Byzantine-robust D2D aggregation protocol that can withstand malicious nodes trying to disrupt model aggregation. To protect privacy, we develop a one-time masking method that eliminates aggregated masks through a dynamic aggregation strategy. This strategy takes into account the adjacency and trust relationships among nodes in evolving network topologies. Finally, we perform a theoretical analysis and evaluate DeSA on real-world datasets. Experimental results show that the time required to verify an embedded proof is significantly reduced compared to the time of verifying multiple proofs. Additionally, its accuracy remains robust against malicious nodes.