Follow blockchain research across journals, conferences, and preprint repositories.
5,430 results · page 34 of 227
Haixing Li, Yutong Zhou, Chi Zhang, Lingbo Wei
No abstract is available for this record.
Longyang Yi, Jian Liu, Zhiguo Wan, Kui Ren · 5 authors
The recent popularity of cryptocurrencies like Bitcoin and Ethereum has drawn widespread attention to the blockchain technique. In particular, some private cryptocurrencies like Zerocash and Monero enhance privacy protection by concealing the identities of participants and transaction amounts. However, such comprehensive privacy measures present regulatory challenges to malicious activities like money laundering and extortion. Therefore, building a novel blockchain that maintains privacy while supporting regulatory oversight is crucial. In this paper, we propose a regulatable and privacy-preserving blockchain scheme that introduces a decoupled and preparatory regulatory process. It serves as a privacy-preserving first line of defense, enabling the identification of anomalous transactions without compromising the confidentiality of the underlying data. Our approach pioneers a method for anomaly screening on private transactions, mitigating risks without resorting to key escrow or content recovery, thus preserving end-to-end privacy for legitimate users. Initially, we explore suitable transaction features within private blockchains for training machine learning classifiers to detect anomalous behaviors. Subsequently, we customize a privacy-centric classifier employing homomorphic encryption to achieve private computation of anomaly detection without leaking sensitive information from private transaction content. We then construct the zero-knowledge proof for validating the encrypted computation process. Our work pioneers in fully integrating homomorphic encryption with zero-knowledge proof, enabling credible and trustworthy verification of the homomorphic ciphertext computations. Finally, we conduct comprehensive security analysis and experimental simulations. The experimental results demonstrate the efficiency and scalability of our approach.
Zounkaraneni Ngoupayou Limbepe, Keke Gai, Jing Yu
Federated learning (FL) has emerged as an efficient machine learning (ML) method with crucial privacy protection features. It is adapted for training models in Internet of Things (IoT)-related domains, including smart healthcare systems (SHSs), where the introduction of IoT devices and technologies can arise various security and privacy concerns. However, as FL cannot solely address all privacy challenges, privacy-enhancing technologies (PETs) and blockchain are often integrated to enhance privacy protection in FL frameworks within SHSs. The critical questions remain regarding how these technologies are integrated with FL and how they contribute to enhancing privacy protection in SHSs. This survey addresses these questions by investigating the recent advancements on the combination of FL with PETs and blockchain for privacy protection in smart healthcare. First, this survey emphasizes the critical integration of PETs into the FL context. Second, to address the challenge of integrating blockchain into FL, it examines three main technical dimensions such as blockchain-enabled model storage, blockchain-enabled aggregation, and blockchain-enabled gradient upload within FL frameworks. This survey further explores how these technologies collectively ensure the integrity and confidentiality of healthcare data, highlighting their significance in building a trustworthy SHS that safeguards sensitive patient information.
Chen-Fan Chang, Ting‐Yu Chang, Chih-Chieh Chang, Te-Chuan Chiu · 6 authors
No abstract is available for this record.
Tumu Rajasekhar Babu
Federated learning represents a transformative approach in the realm of machine learning by enabling the training of models across decentralized devices while maintaining data privacy. Traditional centralized learning methods often compromise user privacy and data security by requiring the aggregation of data on a central server. In contrast, federated learning decentralizes the training process, allowing devices to collaboratively learn a shared model without exposing their private data. This paper explores the intricacies of federated learning, emphasizing its potential to enhance privacy and efficiency in AI systems. We delve into the technical architecture of federated learning, discussing key components such as data partitioning, model aggregation, and communication protocols. Furthermore, we address the challenges associated with federated learning, including data heterogeneity, communication overhead, and model convergence. Through comprehensive analysis and case studies, we demonstrate the efficacy of federated learning in various applications, from healthcare to finance. Our findings underscore the critical role of federated learning in safeguarding data privacy while optimizing the performance of machine learning models. As the demand for privacy preserving technologies continues to grow, federated learning emerges as a pivotal solution, paving the way for more secure and efficient AI systems.
Enzo Fenoglio, Philip Treleaven
No abstract is available for this record.
Shweta Bhardwaj, Yateet Agrawal, Himanshu Chauhan
No abstract is available for this record.
Muhammad Garba
No abstract is available for this record.
Yuji Kawamata, Kaoru Kamijo, Masateru Kihira, Akihiro Toyoda · 8 authors
No abstract is available for this record.
Godwin Olaoye
No abstract is available for this record.
Assiya Akli, Khalid Chougdali
The Internet of Things (IoT) demands robust mechanisms for secure communication and trust establishment among connected devices. Traditional Public Key Infrastructure (PKI) solutions face limitations in scalability, centralization and single points of failure. These limitations hinder their effectiveness in dynamic IoT environments. To address these challenges, this paper introduces a new decentralized authentication protocol for secure identity management and data exchange in IoT, called ISIF (IOTA-Assisted Self-Sovereign Identity Framework). This framework is based on Self-Sovereign Identity (SSI) principles and leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to enable mutual authentication without relying on centralized authorities. DIDs ensure decentralized identity management and VCs provide verifiable context-specific claims. This dual-layer approach enables robust and attribute-based authentication, which reduces the risk of unauthorized access and improving interoperability in decentralized IoT environments. ISIF employs the IOTA Tangle as a distributed ledger to manage and verify DIDs and VCs. This offers a decentralized, immutable record that supports efficient and tamper-resistant identity management. ISIF ensures that all interactions within the IoT network are securely authenticated and resilient to tampering. The experimental results show that the framework maintains efficient DID generation and VC issuance times even as network size scales, overcoming the bottlenecks inherent in PKI-based systems. Experimental results demonstrate that ISIF maintains efficient DID generation and VC issuance, even as network size scales. Experimental results show that DID generation time increases from 1.85 ms (for 50 nodes) to 10.81 ms (for 250 nodes), while VC issuance time ranges from 2.66 ms to 13.21 ms. Similarly, VC verification time increases from 3.54 ms to 22.27 ms as the network scales. Despite these increases, the overall end-to-end (E2E) delay remains low (0.16–0.33 ms), ensuring efficient real-time authentication. These findings confirm ISIF’s feasibility for large-scale IoT authentication without performance degradation. Furthermore, the IOTA Tangle’s performance in handling varied payload sizes affirms its suitability for managing block generation and retrieval in IoT, ensuring practical processing times that uphold security and decentralization.
Omar Isaac Asensio, Catherine E. Moore, Nícola Ulibarrí, Mecit Can Emre Simsekler · 6 authors
Abstract Data for Policy ( dataforpolicy.org ), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.
Mohit Garg
By 2025, an estimated 67.9% of the global population—5.56 billion people—will rely on internet-connected AI tools like ChatGPT to automate tasks, write code, and solve complex problems. While these systems redefine productivity, their centralized architectures pose severe risks: opaque data custodianship, algorithmic surveillance, and vulnerabilities to breaches (e.g., model inversion attacks) have eroded user trust. This paper introduces Decentralized AI Guardians, a framework that merges lightweight AI models with blockchain technology to shift privacy control from corporations to users. At its core, the framework embeds AI “guardians” into blockchain nodes, enabling real-time, context-aware decisions about data access. Each guardian evaluates requests based on factors like app reputation, time, and user history. For instance, it might grant a navigation app daytime location access but deny a social media platform the same privilege at midnight. Permissions are stored on an immutable ledger, eliminating single points of failure. Two innovations ensure privacy and adaptability. First, federated learning allows guardians to refine decision-making collaboratively—edge devices process data locally and share anonymized threat patterns (e.g., phishing trends) without exposing raw information. This reduces latency to 12.3 ms, critical for IoT and mobile applications. Second, zero-knowledge proofs (ZKPs) cryptographically validate compliance without disclosing sensitive details, such as confirming a user’s age without revealing their birthdate. Enforcement is automated via blockchain smart contracts, which penalize violations (e.g., revoking access) and dynamically update policies based on collective AI consensus. For example, if guardians detect a surge in malicious requests disguised as app updates, smart contracts globally block similar activity. Tested against GDPR compliance and adversarial attacks, the framework reduces unauthorized data disclosures by 72% compared to centralized systems while improving threat detection accuracy by 40%. Crucially, it resists “privacy theater”: users cryptographically control their guardians, and AI models are auditable through open-source governance. This transparency ensures accountability, allowing stakeholders to scrutinize decisions and propose upgrades via decentralized voting. By decentralizing control, the framework bridges the gap between static regulations and evolving digital threats. It empowers users to define and enforce privacy rules in real time, offering policymakers a blueprint for scalable, ethical governance. For developers, it provides tools to build AI systems that prioritize user sovereignty over surveillance. In an era of escalating data exploitation, solutions like Decentralized AI Guardians are vital to balancing technological progress with fundamental human rights.
George Teşeleanu
Abstract We present two simple zero-knowledge interactive proofs that can be instantiated with many of the standard decisional or computational hardness assumptions. Compared with traditional zero-knowledge proofs, in our protocols, the verifier starts first, by emitting a challenge, and then, the prover answers the challenge.
Brent Waters, Hoeteck Wee, David J. Wu
No abstract is available for this record.
Jalpesh Vasa, Amit Thakkar, Dev Bhavsar, Pratham Patel
No abstract is available for this record.
M. R. Sumalatha, Aditya Kumar, Nethra Janardhanan, S. Abhinash
No abstract is available for this record.
Zahra Batool, Baturalp Buyukates, Reza Nourmohammadi, Kaiwen Zhang
No abstract is available for this record.
Istiaque Ahmed, Kentaroh Toyoda, Tadashi Nakano, Thi Hong Tran
Traditional digital identity systems struggle with centralization, vulnerability to manipulation, and a lack of transparency. In distributed identity, different cryptographic methods are used for issuing credentials, that create challenges during presentation. It suffer from a fundamental interoperability barrier with heterogeneous digital-signature schemes, forcing each verifier either to implement every scheme or to trust a central translation gateway. We propose a signature-agnostic verification framework that eliminates this barrier. The core idea is to commit a salted root hash of credential claims to a distributed ledger and ensure the authenticity using a smart contract. A zero-knowledge proof (zk-SNARK) is used to prove a selected claim set without revealing actual information. The verification reduces to a single hash-consistency check, and the verifier never touches issuer-specific signatures. A pleasant side effect is that the same verifiable presentation (VP) can be reused across verifiers and sessions, since trust derives from the on-chain anchor rather than transient signatures. This research will advance the identification ecosystem, enabling applications such as eKYC across finance, healthcare, and other sectors. We implement our method on Ethereum Virtual Machine (EVM) using Groth16, benchmark gas cost, proof size, and latency, and show its feasibility and computational efficiency. The privacy and security analysis confirms that the proposed solution is resistant to various attacks.
Meng Li, Yifei Chen, Yan Qiao, Guixin Ye · 7 authors
Blockchain technology autonomously executes smart contracts that require external data to facilitate specific applications, underscoring the necessity for Authenticated Data Feeds (ADF). Existing solutions fall short in providing genuine authentication of data, lack private and verifiable computations across multiple data sources, and overlook data traceability, rendering current systems inadequate for complex applications. We present WuKong (WK), a data governance system that offers authenticated, privately verifiable, and traceable data feeds. WK enables a server to collect faithful data through an oracle committee and to prove computation correctness in zero-knowledge proofs, and empowers legal entities to trace a leakage source conditionally. We formally define and prove the security of WK in the universal composability framework. We implement three applications that seamlessly integrate with WK. Experimental results indicate that WK effectively liberates sensitive data from distributed, untrusted, and anonymous providers, making it accessible to various services and establishing trust in a decentralized world.
Kaijie Jiang, Anyu Wang, Hengyi Luo, Guoxiao Liu · 7 authors
No abstract is available for this record.
Nasim Nezhadsistani, Naghmeh Sadat Moayedian, Burkhard Stiller
Advances in Internet of Medical Things technology, information and communication technologies, and machine learning have initiated the shift in healthcare towards smart healthcare. Centralization of health data to train ML models does pose privacy, ownership, and regulatory problems. Federated learning solves such problems by distributing the learning process to several devices, but it also encounters problems like encouraging participants and model aggregation correctness. Combining blockchain and FL can solve such problems through a decentralized approach that provides greater security and privacy for intelligent healthcare. This survey provides a systematic review of blockchain-based federated learning (BCFL) systems in healthcare. Key design features of BCFLs are analyzed, such as consensus protocols, crypto protocols, storage topology, and integration processes relevant to healthcare use cases. Characteristics such as convergence delay, computation overhead, accuracy loss when privacy is an issue, and ledger scalability for different implementations are compared among common implementations. The works of recent FL-based healthcare frameworks have been discussed along with determining the challenges and research directions for healthcare use cases.
Seid Mehammed, Girma Bewuketu, Demeke Getaneh, Md Nasre Alam · 6 authors
We present a permissioned blockchain–audited federated learning (FL) framework that strengthens data provenance and model‐update integrity. Our contribution is primarily engineering and architectural: a modular two‐channel design (provenance vs. update‐audit), lightweight on‐chain validation with off‐chain analytics, and a practical mapping to the 1 + 5 architectural views. In a TensorFlow Federated + Hyperledger Fabric prototype with 10 clients, we observe ≈18% faster anomaly detection under attack and a + 0.4 pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof‐of‐concept zero‐knowledge succinct noninteractive argument of knowledge (zk‐SNARK) flow to validate per‐client summary properties off‐chain while anchoring results on‐chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.
Haoqi Zhang, Xinjian Chen, Qiong Huang
No abstract is available for this record.