Follow blockchain research across journals, conferences, and preprint repositories.
97,192 results · page 298 of 4,050
Massimo Franceschet, Enrico Bozzo
No abstract is available for this record.
RIYAD SHIKDER
No abstract is available for this record.
Mijian Wang
No abstract is available for this record.
Lingling Wang, Zhongkai Lu, Meng Li, Jingjing Wang · 6 authors
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.
Jintian Dai
No abstract is available for this record.
Ravinjeet Singh
No abstract is available for this record.
June Ma
The dissertation studies how privacy and trust are shaped by digital technologies: how individuals value privacy over personal data, how AI alters trust and disclosure, and how decentralised blockchains can sustainably replace trusted intermediaries. Chapter 1 argues that the 'privacy paradox' --- that individuals claim to value privacy, yet readily disclose personal data --- arises because privacy is treated as monolithic, when it is multidimensional. I develop a framework that distinguishes voluntary disclosure from involuntary data diffusion, reconciling the paradox by showing that disclosures reflect contextual trade-offs. Using a discrete choice experiment, I provide estimates of privacy valuations across both institutional and social contexts. I find that privacy has substantial value when exposure results in harmful consequences, such as socially revealing data reaching close contacts. I also document an AI privacy puzzle: individuals are less concerned about privacy from AI assistants than from the firms that develop them. Chapter 2 examines this AI privacy puzzle. Using a survey experiment, I replicate the finding from Chapter 1 specifically for firms in the AI industry, highlighting the privacy gap that arises despite the clear product--firm relationship. An information treatment that explicitly links AI assistants to their firms increases concern about both, but does not significantly reduce this gap. Instead, the gap also reflects the anthropomorphic features of AI assistants, aversion to the commercial nature of firms, and the trust and perceived control consumers attach to each. However, when respondents evaluate real-world AI assistant--firm pairs, brand familiarity is the strongest predictor of where privacy concern is attributed. Chapter 3 considers decentralised trust in blockchain systems, in which consensus mechanisms replace trusted intermediaries. I propose a 'proof of quiet quitting' consensus mechanism that reduces the excessive energy consumption of proof of work while retaining the decentralisation that proof of stake can compromise. By introducing a participation lottery with unrestricted entry and an endogenous cutoff, the mechanism separates maximum effort capacity from the probability of winning, inducing participants to exert no more than the minimum effort required in equilibrium.
T Krishna, Vadlakonda Sai Vishal, J. Vamsinath
ABSTRACT Efficient disaster response requires scalable, transparent and trustworthy resource management systems. However, centralized approaches frequently suffer from coordination delays, data tampering risks, limited transparency and single points of failure, reducing reliability during large‐scale crises. This study presents a decentralised blockchain‐based framework that integrates smart contracts, decentralised multi‐source oracles for Internet of Things (IoT)‐enabled field reporting, role‐based access control and adaptive urgency scoring to improve allocation prioritisation and trust calibration. The architecture follows a structured three‐tier design. The edge layer supports real‐time sensing and secure data offloading through the Inter‐Planetary File System (IPFS). The blockchain logic layer enforces operational policies, dynamic prioritisation, and reputation scoring using modular, gas‐efficient smart contracts. An integration/API layer ensures secure interoperability among emergency agencies and stakeholders. A hybrid blockchain model combines Ethereum Proof‐of‐Stake (PoS) for public transparency with a permissioned consortium chain for controlled governance. Natural Language Processing (NLP) derives urgency scores from textual disaster reports, while a dynamic supply‐demand aware algorithm adapts resource allocation in real time. Multi‐signature governance and reputation mechanisms further enhance accountability. Experimental evaluation on a simulated testnet demonstrates throughput up to 1000 Transactions Per Second (TPS), alongside measurable improvements in fairness, auditability and allocation efficiency.
Rommel-Santiago Velastegui-Hernández, Raúl Poler, Manuel Díaz-Madroñero
No abstract is available for this record.
Akshara Alagarsamy, Naveenbalaji Gowthaman
DharmaCoin is an artificial intelligence-based cyber fraud detection system that is supposed to establish a safe and moral online financial system. The suggested model enables combining blockchain security, fraud detection with the use of artificial intelligence, biometric multi-factor authentication, and secure document verification to enhance the safeguarding of digital financial crimes. It has a framework built on the values of Indian philosophies based on logic, transparency, and ethical governance and in the name of responsible and trust-worthy digital finance. DharmaCoin manages to identify transaction fraud with a 97% F1-score and forged/altering documents with 99% accuracy in using state-of-the-art technologies SatyaAI, a real-time deepfake and identity-checking tool, and RishiGuard, an anomaly financial activity detecting tool, in 200 milliseconds. The system uses a Proof-of-Stake blockchain registry to ensure unalterable records of transactions, and to increase the level of transparency within financial deals. Performance checks show that the blockchain infrastructure has the capability of handling a rate of transactions of over 1000 transactions in less than five minutes as well as be able to verify transactions in less than five seconds hence scaling to high financial volume environments. A Pilot project involving a banking partner found that a reduction of up to 40 in time to process KYC manually led to a faster approach to operations and better fraud detection. In general, DharmaCoin offers a safe, transparent, sustainable digital finance system, which is a moral and tech-centered solution to the prevention of cyber fraud and promotes the trustful financial ecosystem in accordance with the global sustainability development objectives.
Patrick Woitschig, Ruting Wang, Wolfgang Karl Härdle
No abstract is available for this record.
Samson Ojo
No abstract is available for this record.
C. Ramya, A. Suphalakshmi
The increasing complexity of cyber threats across IoT-cloud infrastructures necessitates the use of innovative, flexible, and confidentiality-preserving prevention techniques. The Blockchain-Assisted Hybrid Attention-Based Intrusion Detection and Access Control System (BHA-IDACS) is presented in this paper. The primary detection module employs an Adaptive Spatio-Temporal Representation Architecture-Self-Attention and Intersample Attention Transformer (Astra-SAINT) to precisely detect evolving intrusion tendencies. A heron optimization algorithm (HOA) is utilized for tuning the model thereby improving accuracy of detection and convergence. Fully Homomorphic Encryption (FHE) maintains the security of data and storage of encrypted data in unsecured cloud and blockchain circumstances. On a Consortium Blockchain, all encrypted transactions and audit trails are maintained by a Proof-of-Stake Authority (PoSA) consensus method. Additionally, based on user behavior and trust level, Smart Contract-Based Dynamic Access Control independently enforces permission and authentication regulations. The suggested model provides better precision, recall, F1-score, F2-score, specificity, and Cohen's Kappa values in addition to a mean accuracy of 99.16%. Furthermore, statistical analysis using confidence intervals and low standard deviation values demonstrates that Astra-SAINT is reliable and consistent across all validation folds. These results demonstrate the efficacy of the suggested Astra-SAINT framework as a scalable and dependable intrusion detection method for protecting IoT environments of the next decade.
Richa Golash, Shahnawaz Ahmad, Bhawana, Naween Kumar
No abstract is available for this record.
Hexiao LI, Jaafar Gaber, Salah Laghrouche
No abstract is available for this record.
R. Bala, S. Gnanavel
The exponential growth of cloud computing has enabled large-scale data outsourcing but has simultaneously introduced critical challenges related to data confidentiality, integrity, and trust. Traditional cryptographic and blockchain-based cloud security solutions often suffer from high computational overhead, latency, and scalability limitations, which hinder their practical adoption. To address these issues, this study proposes a robust and lightweight blockchain-based security framework for secure cloud data storage. The framework integrates hybrid AES–ECC encryption, smart contract–driven access control, and a lightweight consensus mechanism combining Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve efficient and tamper-resistant data management. The proposed system employs an on-chain/off-chain hybrid architecture that stores only essential metadata and cryptographic proofs on the blockchain while maintaining the actual data in distributed cloud storage. This design minimizes computational burden and blockchain bloat while ensuring end-to-end transparency and verifiability. A Merkle tree–based Proof of Storage (PoS) mechanism enables rapid integrity verification without requiring full data retrieval. Comprehensive experiments were conducted using a simulated multi-node cloud environment to evaluate encryption efficiency, transaction latency, throughput, storage overhead, and energy consumption. Results show that the proposed framework outperforms existing blockchain-based models, achieving a 37.7% reduction in encryption/decryption time, a 51.3% decrease in transaction latency, and a 54.5% improvement in energy efficiency. Additionally, the system attained a 99.3% security success rate under various attack scenarios, demonstrating its resilience against unauthorized access, replay, and tampering attempts. These findings confirm that the proposed approach provides a practical balance between security assurance and performance optimization.
Manaswitha Cherukuri, Purnima Ahirao
No abstract is available for this record.
P. Manju Bala, S. Usharani, A. Balachandar, A. Olukayode
No abstract is available for this record.
Tommaso Brollo, Giuseppe De Luca
No abstract is available for this record.
John Manuel Barrios, Christoph Bertsch, Linda Schilling
No abstract is available for this record.
S. B. Goyal, Anand Singh Rajawat, Jaiteg Singh, Hayyan Nassar Waked
No abstract is available for this record.
Viktoriya A. Evseenko
Subject. This article discusses the peculiarities of forming a holistic approach to assessing human capital as an economic resource of regions to improve the efficiency of its use in the context of decentralization. Objectives. The article aims to study human capital as a factor in the economic growth of regions in the context of decentralization, identify interregional disparities in its development, and substantiate strategic areas for intensifying the formation, funding, and effective use of human potential at the territorial level. Methods. For the study, I used analysis and synthesis, induction and deduction, abstraction, comparison, generalization, and systems and dialectical methods. Results. The article proposes strategic areas for intensifying and financing the development of human capital, taking into account modern challenges, and it pays particular attention to the institutional capacities of regions. The article substantiates current approaches to financing human capital in the context of the transition to a decentralization model, as well as in the systematization of data on investments in human resources for 20232025, and it formulates proposals for improving the sustainability of regional development through the optimization of inter-budgetary transfers, taking into account the existing interregional imbalances in the Human Development Index. Relevance. The results obtained have a certain practical value, as they can help regions more effectively utilize their potential, attract investment, create new jobs, and develop their own unique strategies for human capital development based on territorial characteristics.
Irene Aldridge
No abstract is available for this record.
Toshisada Utsunomiya
No abstract is available for this record.