Protecting user rights and enabling cross-border interoperability require the existence of private and secure management of personal identity data in digital environments. To address this need in the context of electronic health cards for digital identification and healthcare services, this paper proposes a novel architecture that integrates self-sovereign identity (SSI), verifiable credentials (VC), and zero- knowledge proofs (ZKP). With SSI, users maintain full control over their data, storing and sharing identity credentials through digital wallets using decentralized identifiers (DIDs). VC enables trusted organizations to issue authenticated, tamper-proof digital certificates, while ZKPs allow individuals to prove identity-related claims without disclosing unnecessary personal details. The combination of these technologies enables a framework that ensures a tradeoff between user privacy and security processes. The findings of the tests achieved indicate that our framework, compared to conventional models, optimizes the interoperability and privacy of e-health card systems. Additionally, the suggested solution supports recent data protection regulations and demonstrates strong potential for global adoption.
Dadmehr Rahbari, Masoud Daneshtalab, Maksim Jenihhin
With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized training. To tackle the typical challenges of FLâsuch as high communication overhead, resource limitations, and convergence inefficienciesâthe method integrates dynamic client clustering, quantization-aware training, and structured model pruning. These optimizations collectively reduce latency and memory consumption while accelerating model convergence. Evaluations using the Human Activity Recognition dataset show that the proposed approach outperforms several state-of-the-art FL methods, achieving an average +8.4% improvement in accuracy, -16.2% lower latency, -35.1% reduction in memory usage, and -2.7% lower security overhead. These results demonstrate its suitability for real-time, resource-constrained scenarios in domains like healthcare, IoT, and finance, where maintaining a strong balance between efficiency and privacy is essential.
The rapid advancement of technology and the widespread adoption of various IoT devices have resulted in significant challenges in handling and analyzing the vast and diverse volumes of data they produce. Conventional machine learning methodologies, which require data aggregation in a centralized repository for analysis, are often unfeasible due to the significant data volume, limited communication bandwidth, and strict security and privacy requirements. Federated Learning (FL) presents an innovative resolution by facilitating data analysis to take place directly at the data origin, federating the outcomes to generate results comparable to centralized processing. Due to the advancements in growth of federated learning architecture, a number of open-source frameworks have been established to implement this strategy. These frameworks facilitate a range of applications, including healthcare and finance, as well as IoT and smart city initiatives, by enabling data to remain decentralized while still supporting the development of comprehensive analytical models. This study provides a comprehensive evaluation of these established open-source FL frameworks, examining their applicability across various sectors and highlighting their advantages and limitations. The authors examine the various architectural structures, supported algorithms, level of implementation ease, and community support of each framework. The assessment additionally takes into account the scalability of these frameworks and their flexibility in various data scenarios. Through the assessment of the current capabilities and developmental phases of these FL frameworks, this study aims to support practitioners and researchers in identifying the most suitable tools for their specific needs. This analysis highlights the significance of FL in facilitating secure and effective data analysis in the contemporary technological landscape.
Brugeres, Maxence, Languille, Victor, Kuznetsov, Petr, Zarfaoui, Hamza
We propose a decentralized asset-transfer system that enjoys full privacy: no party can learn the details of a transaction, except for its issuer and its recipient. Furthermore, the recipient is not aware of the senderâs identity. Our system does not rely on consensus or synchrony assumptions, and therefore, it is responsive, since it runs at the actual network speed. Under the hood, every transaction creates a consumable coin equipped with a non-interactive zero-knowledge proof (NIZK) that confirms that the issuer has sufficient funds without revealing any information about her identity, the recipientâs identity, or the payment amount. Moreover, we equip our system with a regulatory enforcement mechanism that can be used to regulate transfer limits or restrict specific addresses from sending or receiving funds, while preserving the systemâs privacy guarantees. Finally, we report on PaxPay, our implementation of Fully Private Asset Transfer (FPAT) that uses the Gnark library for the NIZKs. In our benchmark, PaxPay exhibits better performance than earlier proposals that either ensure only partial privacy, require some kind of network synchrony or do not implement regulation features. Our system thus reconciles privacy, responsiveness, regulation enforcement and performance.
Increasing attention to digital identity and self-sovereign identity (SSI) is gaining momentum. SSI brings various benefits to natural persons, such as owning controls; conversely, digital identity systems in the real world require Sybil-resistance to comply with anti-money laundering (AML) and other needs. CanDID by Maram et al. proposed that decentralized digital identity systems may achieve Sybil-resistance and preserve privacy by utilizing multi-party computation (MPC), assuming a distributed committee of trusted nodes. Pass et al. proposed the formal abstraction of attested execution secure processors (AESPs) while equipping hardware-assisted security in mobile devices has become the norm. We first describe our proposal to utilize AESPs for building secure Sybil-resistant SSI systems, the architecture with a set of system protocols$\Pi ^{{\mathcal {G}}_{\mathtt {att}}}$, which brings drastic flexibility and efficiency compared to existing systems. In addition, we propose a novel scheme that enables users (holders) to request verifiers to verify their credentials without AESPs, and it further achieves unlinkability among credentials created for public verification. Our scheme introduces a simplified format for computed claims and commitment-based anonymous identifiers. We also describe a technique to utilize zero-knowledge membership proofs, in particular, âOne-Out-of-Many Proofsâ$\Sigma $-protocol by Groth and Kohlweiss, which can prove the existence of an expected credential without identifying it. Along with other techniques, such as utilizing the BBS+ signature scheme, we demonstrate how our scheme can achieve its goals with the extended anonymous and Sybil-resistant SSI system protocols$\Pi ^{{\mathcal {G}}_{\mathtt {att}}+}$. Entitling unlinkability among derived credentials in the anonymous Sybil-resistant SSI results in proper privacy preservation.
In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.
Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (â $0.18) for linear models, 143,647 gas (â $0.49) for CNN(F2, K1), and 506,397 gas (â $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening.
Elizaveta Pertseva, Alex Ozdemir, Shankara Pailoor, Alp Bassa · 7 authors
Abstract This paper presents a new refutation procedure for multimodular systems of integer constraints that commonly arise when verifying cryptographic protocols. These systems, involving polynomial equalities and disequalities modulo different constants, are challenging for existing solvers due to their inability to exploit multimodular structure. To address this issue, our method partitions constraints by modulus and uses lifting and lowering techniques to share information across subsystems, supported by algebraic tools like weighted Gr bner bases. Our experiments show that the proposed method outperforms existing state-of-the-art solvers in verifying cryptographic implementations related to Montgomery arithmetic and zero-knowledge proofs.
With the increasing use of online services, the protection of the privacy of users becomes more and more important. This is particularly critical as authentication and authorization as realized on the Internet nowadays, typically relies on centralized identity management solutions. Although those are very convenient from a user's perspective, they are quite intrusive from a privacy perspective and are currently far from implementing the concept of data minimization. Fortunately, cryptography offers exciting primitives such as zero-knowledge proofs and advanced signature schemes to realize various forms of so-called anonymous credentials. Such primitives allow to realize online authentication and authorization with a high level of built-in privacy protection (what we call privacy-preserving authentication). Though these primitives have already been researched for various decades and are well understood in the research community, unfortunately, they lack widespread adoption. In this paper, we look at the problems, what cryptography can do, some deployment examples, and barriers to widespread adoption. Latter using the example of the EU Digital Identity Wallet (EUDIW) and the recent discussion and feedback from cryptography experts around this topic. We also briefly comment on the transition to post-quantum cryptography.
Dakai Kang, Junchao Chen, Tien Tuan Anh Dinh, Mohammad Sadoghi
The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain database technology, with smart contracts enabling the growth of decentralized finance (DeFi). However, research has shown that adversaries exploit transaction ordering to extract profits through attacks like front-running, sandwich attacks, and liquidation manipulation. This issue affects blockchains where block proposers have full control over transaction ordering. To address this, a more fair transaction ordering mechanism is essential. Existing fairness protocols, such as Pompe and Themis, operate on leader-based consensus protocols, which not only suffer from low throughput caused by the single-leader bottleneck, but also allow adversarial block proposers to manipulate transaction ordering. To address these limitations, we propose a new framework, FairDAG, that runs fairness protocols on top of DAG-based consensus protocols. FairDAG improves protocol performance in both throughput and fairness quality by leveraging the multi-proposer design and validity property of DAG-based consensus protocols. We conducted a comprehensive analytical and experimental evaluation of two FairDAG variants - FairDAG-AB and FairDAG-RL. Our results demonstrate that FairDAG outperforms prior fairness protocols in both throughput and fairness quality.
Pavloff, Ulysse, Amoussou-Guenou, Yackolley, Tucci-Piergiovanni, Sara
This paper investigates whether following the fork-choice rule in the Ethereum PoS consensus protocol constitutes a Nash equilibrium - i.e., whether the protocol that maintains the canonical chain in Ethereum is incentive-compatible. Specifically, we explore whether selfish participants may attempt to manipulate the fork-choice rule by forking out previous blocks and capturing the rewards associated with those blocks. Our analysis considers two strategies for participants: the obedient strategy, which adheres to the prescribed protocol, and the cunning strategy, which attempts to manipulate the fork-choice rule to gain more rewards. We evaluate the conditions under which selfish participants might deviate from the obedient strategy. We found that, in a synchronous system, following the prescribed fork-choice rule is incentive-compatible. However, in an eventually synchronous system, the protocol is eventually incentive-compatible - that is, only a limited number of proposers will find it profitable to fork the chain during the synchronous period. After this sequence of cunning proposers, subsequent proposers will find it more profitable to follow the protocol.
Non-Fungible Tokens (NFT) represent a sub form of cryptocurrencies, which allow investments into art, digital rights or any form of unique assets. Our study adds to a young body of research, which tries to analyze the characteristics of these new markets. In our study we present a new methodology to analyze peer-to-peer transactions in Non-Fungible tokens (NFT). We argument that this approach is superior to using prefabricated market data from NFT platform data, because it allows to track transactions to the point 0. Compared to that prefabricated market data could prove to be incomplete, missing peer-to-peer transactions. The difficulty of our approach was to develop a methodology which looks at all available transactions of a NFT on the blockchain, and not just at transactions which take place at specific trading platforms. This research is challenging due to the amount, and complexity of blockchain data which need to be stored and analyzed. For our study we constructed a dataset for the eth blockchain, which represented a complete chain of blockchain transactions, while then comparing our data with platform data on specific NFTâs. We found that our approach provides a more detailed view of the transactions in specific NFTâs. This detailed view allowed us to see specific transaction details. Our approach shall inspire future research to favor complete datasets of NFT transactions, before easily available data from trading platforms.
Common blockchain protocols are monolithic, i.e., their security relies on a single assumption, e.g., honest majority of hashing power (Bitcoin) or stake (Cardano, Algorand, Ethereum). In contrast, so-called optimistic approaches (Thunderella, Meshcash) rely on a combination of assumptions to achieve faster transaction liveness. We revisit, redesign, and augment the optimistic paradigm to a tiered approach. Our design assumes a primary (Tier 1) and a secondary (Tier 2, also referred to as fallback) blockchain, and achieves full security also in a tiered fashion: If the assumption underpinning the primary chain holds, then we guarantee safety, liveness and censorship resistance, irrespectively of the status of the fallback chain. And even if the primary assumption fails, all security properties are still satisfied (albeit with a temporary slow down) provided the fallback assumption holds. To our knowledge, no existing optimistic or tiered approach preserves both safety and liveness when any one of its underlying blockchain (assumptions) fails. The above is achieved by a new detection-and-recovery mechanism that links the two blockchains, so that any violation of safety, liveness, or censorship resistance on the (faster) primary blockchain is temporary - it is swiftly detected and recovered on the secondary chain - and thus cannot result in a persistent fork or halt of the blockchain ledger. We instantiate the above paradigm using a primary chain based on proof of reputation (PoR) and a fallback chain based on proof of stake (PoS). Our construction uses the PoR and PoS blockchains in a mostly black-box manner - where rather than assuming a concrete construction we distil abstract properties on the two blockchains that are sufficient for applying our tiered methodology. In fact, choosing reputation as the resource of the primary chain opens the door to an incentive mechanism - which we devise and analyze - that tokenizes reputation in order to deter cheating and boost participation (on both the primary/PoR and the fallback/PoS blockchain). As we demonstrate, such tokenization in combination with interpreting reputation as a built-in system-wide credit score, allows for embedding in our two-tiered methodology a novel mechanism which provides collateral-free, multi-use payment-channel-like functionality where payments can be instantly confirmed.
Edward Danso Ansong, Simon Bonsu Osei, Raphael Adjetey Adjei
The surge in identity fraud, driven by the rapid adoption of mobile money, internet banking, and e-services during the COVID-19 pandemic, underscores the need for robust cybersecurity solutions. Zero-Knowledge Proofs (ZKPs) e... | Find, read and cite all the research you need on Tech Science Press
Blockchain technology has revolutionized secure and decentralized digital transactions. However, the emergence of quantum computing presents a significant threat to traditional cryptographic protocols, particularly public-key encryption mechanisms such as RSA and Elliptic Curve Cryptography (ECC). Quantum computers, leveraging Shorâs and Groverâs algorithms, can efficiently break these encryption schemes, compromising blockchain security. This paper explores quantum-secure blockchain protocols that integrate post-quantum cryptographic (PQC) techniques such as lattice-based, hash-based, and code-based cryptography to resist quantum attacks. Additionally, we evaluate quantum-resistant consensus mechanisms like Quantum-Secure Proof of Stake (QS-PoS) and Quantum-Protected Byzantine Fault Tolerance (Q-BFT). Through simulation-based performance analysis, we demonstrate that quantum-safe blockchain models can achieve robust security while maintaining efficient transaction processing. Our findings suggest that a hybrid approach, combining classical cryptographic elements with post-quantum algorithms, provides the best balance between security, performance, and scalability.
Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data poisoning, model inversion, membership inference attacks, and communication interception. These attacks compromise the confidentiality of patients in healthcare, consumer data privacy in e-commerce, and financial safety in banking, thus necessitating effective privacy-preserving mechanisms. This survey presents a classification of security threats in FL, grouping them by their source, effect, and attack mode. We review state-of-the-art countermeasures, such as differential privacy, secure multi-party computation, homomorphic encryption, and resilient aggregation methods, their effectiveness, trade-offs, and real-world applicability to FL. In medicine, FL enables joint disease diagnosis without compromising patient confidentiality; in online shopping, it provides personalized suggestions without revealing customer tastes; and in banking, it improves fraud detection without violating regulatory requirements. In addition, we discuss future horizons in privacy-preserving FL, including adversarial robustness, blockchain-protected models, and tailored FL architectures, improving security and resiliency in these domains. We also discuss the balancing problems between security, accuracy, and computational efficiency with possible trade-offs in scaling privacy-preserving FL By analyzing threats and mitigation strategies systematically, this paper will provide direction to future research on designing secure, scalable, and privacy-preserving FL frameworks for the changing healthcare, e-commerce, and finance needs.
Open access
Privacy-Preserving Technologies in Data
Cryptography and Data Security
Artificial Intelligence in Healthcare and Education