Follow blockchain research across journals, conferences, and preprint repositories.
5,430 results · page 39 of 227
Scott Griffy, Anna Lysyanskaya, Omid Mir, Octavio Pérez Kempner · 5 authors
No abstract is available for this record.
Aparna Singh, Geetanjali Rathee
No abstract is available for this record.
Renas Bacho, Benedikt Wagner
No abstract is available for this record.
Tingyu Fan, Xiaojun Chen, Ye Dong, Xudong Chen · 6 authors
Existing federated learning (FL) systems are highly vulnerable in terms of security and privacy due to their distributed architecture, facing poisoning attacks and inference attacks from adversaries. Some prior works have combined poisoning defenses with cryptographic tools: Secure Multi-Party Computation, Zero-Knowledge Proof, and Homomorphic Encryption to propose robust secure aggregation methods that provide security and privacy preservation for FL. Recently, Qin et al. (KDD’23) demonstrate that personalized federated learning (pFL) can effectively resist backdoor injection in poisoning attacks. In this paper, we analyze that as the number of malicious attackers increases, pFL remains vulnerable to backdoor attacks. Moreover, we reveal that current robust secure aggregation methods fail to offer efficient and robust backdoor defense for pFL. Therefore, we propose FLIGHT, a robust secure aggregation method for pFL. It implements a lightweight backdoor detection through a two-stage personalized defense mechanism and ensures privacy preservation using communication-efficient two-party secure computation (2PC) protocols. Extensive experiments on diverse datasets and neural networks validate that FLIGHT decreases run-time up to 64× compared by prior work RoFL (S&P’23), and 42× compared to FLAME (USENIX Security’22).
Osama Elghazaly, Nidal Nasser, Ahmed El Ouadrhiri, Asmaa Ali
Blockchain-based smart contracts, while transformative, pose privacy concerns due to Ethereum's transparency. To address this, we present Safe Smart Contracts (SafeSC), leveraging zk-SNARKs for privacy without compromising Ethereum's transparency. SafeSC's Python tool facilitates contract understanding and verification without accessing the source code. Our paper explores privacy preservation techniques, favoring Zero-Knowledge Proofs (ZKPs). SafeSC employs zk-SNARKs and Groth-16, achieving a delicate balance between transparency and privacy in smart contract development. The tool’s design, covering architecture, assumptions, data flow, and zero-knowledge proof workflow, marks a step toward secure smart contract solutions. We advocate for continued exploration and refinement to enhance blockchain technologies.
Diego F. Aranha, Anamaria Costache, Antonio Guimarães, Eduardo Soria-Vázquez
No abstract is available for this record.
Fuchun Joseph Lin, Chaoping Xing, Yizhou Yao
No abstract is available for this record.
Shenoy Phalgun, M. Durga, Amit Dua, Gagangeet Singh Aujla · 5 authors
Different types of cryptocurrencies have surged rapidly in recent years, with recent trends in tokenization and memecoin. This growth highlights the cryptocurrency market’s rapid expansion, especially in token creation at decentralized exchanges (DEXs). At the same time, the evolution of artificial intelligence (AI) and Web3 presents significant challenges, including sustained user engagement in AI-driven applications while maintaining utility and scalability. Existing game theory-based methods often increase centralization and hence introduce security vulnerabilities. This hinders widespread adoption. To overcome these challenges, we propose Slinky Web3AI, a framework that integrates blockchain technology with AI to address these challenges. Slinky Web3AI supports long-term ecosystem development through a decentralized token creation mechanism and privacy-preserving incentive structures. Our proposed architecture demonstrates improved security, scalability, and efficiency, validated by experimental results. Slinky Web3AI provides a foundational framework for future AI and blockchain applications for future smart communities.
Shiva Mehta, Amanveer Singh
Integrating both federated learning and blockchain technology proposed a writing solution to the inescapable challenges of data privacy, security, and trust in FL-based decentralized machine learning environments. This paper proposes a BEFL framework with a blockchain network to ensure the trustworthiness and accountability of federated learning. The results presented in the experimental evaluation, conducted on MNIST, CIFAR-10, and a healthcare dataset, demonstrate the superiority of BEFL over FL. The BEFL framework achieved 98.5% on MNIST, CIFAR-10 82.3%, and the healthcare dataset 87.6%while surpassing standard FL by 3% on average. Regarding the convergence speed, the proposed BEFL reached the target accuracy level in 40 communication cycles for MNIST and 100 rounds for CIFAR-10, while the standard FL was 60 and 140 rounds, respectively, which is approximately 30% faster. While BEFL has a higher communication cost (180 MB for MNIST and 300 MB for CIFAR- 10) than the standard FL (150 MB and 250 MB, respectively), the authors consider it a worthy tradeoff with benefits in terms of security and transparency. The above results under adversary scenarios showed that, as with FL, the proposed BEFL framework was more robust to data poisoning, with the accuracy drop to a mere 1.5% as opposed to 5.2% of FL and a model inversion attack reconstruction accuracy of only 20% as compared to 60% of FL. The above outcomes show that through BEFL, it is possible to support distributed learning while maintaining the security of the fields of study, such as health and finance, as illustrated above.
Yu‐Wen Cheng, Yijun Jing, Zicheng Shi, Chuan-Kun Wu
In recent years, protecting data privacy in distributed environments has become a key challenge with the rise of the Internet of Things (IoT) and edge computing. Federated learning helps mitigate the risk of data leakage by keeping data local, but issues such as authentication and insufficient privacy protection remain. In this paper, we propose a cloud-edge-end federated learning privacy protection scheme (FL-DPASS) that incorporates differential privacy and secret sharing. We introduce a Schnorr zero-knowledge proof authentication mechanism to secure end devices' access without revealing their identities. Additionally, we combine differential privacy and secret sharing to enhance security during model parameter transmission and aggregation. Experimental results demonstrate that our scheme strengthens data privacy protection while maintaining training efficiency and accuracy.
Marco Gomes, Luís Costa, Patrícia Anjos Azevedo
No abstract is available for this record.
Gholamreza Ramezan, Eladio Robles Casas, Ben Beath, Jake Godfrey
No abstract is available for this record.
Muhammad Kashif, Kübra Kalkan
No abstract is available for this record.
Liang Zhang, Zhanrong Ou, Changhui Hu, Haibin Kan · 5 authors
Data sharing is ubiquitous in the metaverse, which adopts blockchain as its foundation. Blockchain is employed because it enables data transparency, achieves tamper resistance, and supports smart contracts. However, securely sharing data based on blockchain necessitates further consideration. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising primitive to provide confidentiality and fine-grained access control. Nonetheless, authority accountability and key abuse are critical issues that practical applications must address. Few studies have considered CP-ABE key confidentiality and authority accountability simultaneously. To our knowledge, we are the first to fill this gap by integrating non-interactive zero-knowledge (NIZK) proofs into CP-ABE keys and outsourcing the verification process to a smart contract. To meet the decentralization requirement, we incorporate a decentralized CP-ABE scheme into the proposed data sharing system. Additionally, we provide an implementation based on smart contract to determine whether an access control policy is satisfied by a set of CP-ABE keys. We also introduce an open incentive mechanism to encourage honest participation in data sharing. Hence, the key abuse issue is resolved through the NIZK proof and the incentive mechanism. We provide a theoretical analysis and conduct comprehensive experiments to demonstrate the feasibility and efficiency of the data sharing system. Based on the proposed accountable approach, we further illustrate an application in GameFi, where players can play to earn or contribute to an accountable DAO, fostering a thriving metaverse ecosystem.
Siyue Li, Kele Niu, Bin Wu
No abstract is available for this record.
P. Surendar, Adhithi Satish Kumar, Venkata Pavani, M. Azhagiri
No abstract is available for this record.
Saurav Ghosh, Reshmi Mitra, Indranil Roy, Bidyut Gupta
Ensuring security for highly dynamic peer-to-peer (P2P) networks has always been a challenge, especially for services like online transactions and smart devices. These networks experience high churn rates, making it difficult to maintain appropriate access control. Traditional systems, particularly Role-Based Access Control (RBAC), often fail to meet the needs of a P2P environment. This paper presents a blockchain-based access control framework that uses Ethereum smart contracts to address these challenges. Our framework aims to close the gaps in existing access control systems by providing flexible, transparent, and decentralized security solutions. The proposed framework includes access control contracts (ACC) that manage access based on static and dynamic policies, a Judge Contract (JC) to handle misbehavior, and a Register Contract (RC) to record and manage the interactions between ACCs and JC. The security model combines impact and severity-based threat assessments using the CIA (Confidentiality, Integrity, Availability) and STRIDE principles, ensuring responses are tailored to different threat levels. This system not only stabilizes the fundamental issues of peer membership but also offers a scalable solution, particularly valuable in areas such as the Internet of Things (IoT) and Web 3.0 technologies.
Liang Wang, Jianping Li, Lina Zuo
No abstract is available for this record.
M. N. Triet, L. K. Bang, H. V. Khanh, Trần Ngọc Anh · 8 authors
No abstract is available for this record.
Fengjun Shang, Xinxin Deng
No abstract is available for this record.
Josh Beal, Ben Fisch
A privacy pool enables clients to deposit units of a cryptocurrency into a shared pool where ownership of deposited currency is tracked via a system of cryptographically hidden records. Clients may later withdraw from the pool without linkage to previous deposits. Some privacy pools also support hidden transfer of currency ownership within the pool. In August 2022, the U.S. Department of Treasury sanctioned Tornado Cash, the largest Ethereum privacy pool, on the premise that it enables illicit actors to hide the origin of funds, citing its usage by the DPRK-sponsored Lazarus Group to launder over $455 million dollars worth of stolen cryptocurrency. This ruling effectively made it illegal for U.S. persons/institutions to use or accept funds that went through Tornado Cash, sparking a global debate among privacy rights activists and lawmakers. Against this backdrop, we present Derecho, a system that institutions could use to request cryptographic attestations of fund origins rather than naively rejecting all funds coming from privacy pools. Derecho is a novel application of proof-carrying data, which allows users to propagate allowlist membership proofs through a privacy pool's transaction graph. Derecho is backwards-compatible with existing Ethereum privacy pool designs, adds no overhead in gas costs, and costs users only a few seconds to produce attestations.
Ny Hasina Andriambelo, Naghmeh Moradpoor
The rise of Autonomous Vehicles (AVs) brings with it the need for secure and privacy-preserving machine learning models. Federated Learning (FL) allows AVs to collaboratively train models while keeping raw data localized. However, traditional FL systems are vulnerable to security threats, including adversarial attacks, data breaches, and dependency on a central aggregator, which can be a single point of failure. To address these concerns, this paper introduces a peer-to-peer decentralized federated learning system that integrates lightweight blockchain technology and Binius Zero- Knowledge Proofs (ZKPs) to enhance security and privacy. In this system, Binius ZKPs ensure that model updates are cryptographically verified without exposing sensitive information, guaranteeing data confidentiality and integrity during the learning process. The lightweight blockchain framework secures the network by creating an immutable, decentralized record of all model updates, thus preventing tampering, fraud, or unauthorized modifications. This decentralized approach eliminates the need for a central aggregator, significantly enhancing system resilience to attacks and making it suitable for dynamic environments like AV networks. Additionally, the system's design includes Byzantine resilience, providing protection against adversarial nodes and ensuring that the global model aggregation process remains robust even in the presence of malicious actors. Extensive performance evaluations demonstrate that the system achieves low-latency, scalability, and efficient resource usage while maintaining strong security and privacy guarantees, making it an ideal solution for real-time federated learning in autonomous vehicle networks. The proposed framework not only ensures privacy but also fosters trust among participants in a fully decentralized environment.
Guofeng Tang, Shuai Han, Li Lin, Changzheng Wei · 5 authors
With the demand of cryptocurrencies, threshold ECDSA recently regained popularity. So far, several methods have been proposed to construct threshold ECDSA, including the usage of OT and homomorphic encryptions (HE). Due to the mismatch between the plaintext space and the signature space, HE-based threshold ECDSA always requires zero-knowledge range proofs, such as Paillier and Joye-Libert (JL) encryptions. However, the overhead of range proofs constitutes a major portion of the total cost.
Ron Steinfeld, Amin Sakzad, Muhammed F. Esgin, Veronika Kuchta · 6 authors
We introduce the first candidate Lattice-based designated verifier (DV) zero knowledge sUccinct Non-interactive Argument (ZK-SNARG) protocol, named LUNA, with quasi-optimal proof length (quasi-linear in the security/privacy parameter). By simply relying on mildly stronger security assumptions, LUNA is also a candidate ZK-SNARK (i.e. argument of knowledge). LUNA achieves significant improvements in concrete proof sizes, reaching below 6 KB (compared to >32 KB in prior work) for 128-bit security/privacy level. To achieve our quasi-optimal succinct LUNA, we give a new regularity result for 'private' re-randomization of Module LWE (MLWE) samples using discrete Gaussian randomization vectors, also known as a lattice-based leftover hash lemma with leakage, which applies with a discrete Gaussian re-randomization parameter that is polynomial in the statistical privacy parameter (avoiding exponential smudging), and hides the coset of the re-randomization vector support set. Along the way, we derive bounds on the smoothing parameter of the intersection of short integer solution (SIS), gadget, and Gaussian perp module lattices over the power of 2 cyclotomic rings. We then introduce a new candidate linear-only homomorphic encryption scheme called Module Half-GSW (HGSW), and apply our regularity theorem to provide smudging-free circuit-private homomorphic linear operations for Module HGSW. Our implementation and experimental performance evaluation show that, for typical instance sizes, Module HGSW provides favourable performance for ZK-SNARG applications involving lightweight verifiers. It enables significantly (around 5x) shorter proof lengths while speeding up CRS generation and encryption time by 4-16x and speeding up decryption time by 4.3x, while incurring just 1.2-2x time overhead in linear homomorphic proof generation operations, compared to a Regev encryption used in prior work in the ZK-SNARG context. We believe our techniques are of independent interest and will find application in other privacy-preserving lattice-based protocols.