Blockchain Papers

Moumita Dutta, Chaya Ganesh, Neha Jawalkar

No abstract is available for this record.

Prabhanjan Ananth, Gilad Asharov, Vipul Goyal, Hadar Kaner · 6 authors

No abstract is available for this record.

Nawras H. Sabbry, Алла Левина

<abstract> <p>This paper proposes a deterministic nonce generation technique to address the catastrophic issues associated with nonce reuse in message signing and to enhance the efficiency of Schnorr multi-signature schemes. Additionally, this research aims to reduce computational complexity and bandwidth requirements in digital and multi-signature schemes while maintaining robust security against common attacks. The proposed method was inspired by the EdDSA approach. The methodology includes a comprehensive mathematical analysis of digital signature algorithms and a rigorous examination of their vulnerabilities to well-known cryptographic attacks. This analysis evaluates the effectiveness and robustness of the proposed nonce generation technique within the frameworks of the Schnorr digital signature and the two-round MuSig schemes. Techniques and tools employed in this research involve deterministically generating nonces by hashing the private key and subsequently hashing the result with the message. Furthermore, it is proposed to exclude the public nonce R from the challenge calculations and to allow signers to directly prove possession of their secret keys through the aggregated public key, thereby eliminating the need for non-interactive zero-knowledge (NIZK) proofs. The findings demonstrate significant reductions in computational complexity and operational requirements, thereby improving bandwidth efficiency and making this method well-suited for resource-constrained devices. The approach also exhibits strong resistance to various attacks, including nonce reuse, key cancellation, rogue keys, and virtual machine rewinding.</p> </abstract>

Masayuki Abe, Andrej Bogdanov, Miyako Ohkubo, Alon Rosen · 6 authors

No abstract is available for this record.

Shuai Han, Shengli Liu, Dawu Gu

No abstract is available for this record.

Apurva K. Vangujar, Alia Umrani, Paolo Palmieri

No abstract is available for this record.

Yueyue He, Jiageng Chen, Koji Inoue

No abstract is available for this record.

Saloni Jain, Ashwija Reddy Korenda, Amisha Bagri, Bertrand Cambou · 5 authors

No abstract is available for this record.

Rocco de Filippis, Abdullah Al Foysal

In an era marked by rapid technological advancement, the fusion of Artificial Intelligence (AI), Machine Learning (ML), and Distributed Ledger Technology (DLT), commonly referred to as blockchain, represents a pioneering frontier in healthcare and psychology.This paper explores the transformative potential of integrating these technologies to reimagine traditional practices and unlock novel approaches to patient care, diagnostics, therapy, and mental health management.Specifically, it investigates the unique and complementary roles that AI, ML, and DLT can play within healthcare and psychology, presenting a detailed roadmap for researchers, practitioners, and stakeholders.Through AI and ML's advanced analytics and predictive capabilities, and blockchain's secure, decentralized data management, this paper demonstrates how these technologies can collectively enhance diagnostic precision, personalize treatment plans, optimize resource allocation, and streamline administrative workflows.Central to this study is a proposed technical architecture, illustrating how AI, ML, and DLT can be integrated within healthcare workflows.This includes using blockchain for secure, verifiable patient data storage and off-chain AI/ML processing for real-time, data-driven insights.Additionally, this paper discusses practical methods, such as zero-knowledge proofs and federated learning, to maintain privacy and regulatory compliance in handling sensitive health data, especially in mental health contexts.Addressing the importance of ethical considerations, this paper highlights best practices in responsible innovation, emphasizing transparency, accountability, and fairness in the deployment of these technologies.Compliance with frameworks like GDPR and HIPAA is discussed as crucial for ensuring patient rights and establishing trust in data handling practices.Moreover, the paper underscores the need for interdisciplinary collaboration, identifying structured models for joint efforts between healthcare professionals, data scientists, and blockchain developers.Examples include cross-disciplinary training sessions, shared project management How to cite this paper:

He Qin, Xiaofeng Ma, Dawei Zhang, Feng Peng

Decentralized identity represents an innovative approach based on blockchain to achieve effective identity management. This method utilizes decentralized identifiers and verifiable credentials to enable trusted authentication, free circulation of identity information, and self-sovereign control over identity data functionalities. The current decentralized identity systems rely on entirely anonymous identifiers, lacking robust identity regulation. Furthermore, they face challenges such as identity attribute leakage during verifiable credential presentation and the issuers’ struggle to reliably revoke credentials. To address these issues, efficient and practical schemes have been designed based on BBS signature, zero-knowledge proof, dynamic accumulator, and blockchain technology: one for decentralized identifiers management and the other for verifiable credential privacy protection, both of which are supervised and revocable. The former ensures the privacy of subject identity while achieving regulatability and revocability of identity data by the regulator. The latter facilitates selective disclosure of anonymous credentials and reliable revocation. A security analysis shows that the proposed scheme meets anonymity, non-forgeability, regulatory reliability, and revocability reliability, and offers comprehensive and effective privacy protection measures. The experimental results demonstrate that the algorithms designed operate at a millisecond level, which satisfies the demands of blockchain identity management scenarios.

Oskar Petto, Thomas Preindl, Martin Kjäer

No abstract is available for this record.

Tassos Dimitriou

No abstract is available for this record.

Nidhish Bhimrajka, Ashish Choudhury, Supreeth Varadarajan

No abstract is available for this record.

Ashwija Reddy Korenda, Saloni Jain, Bertrand Cambou

No abstract is available for this record.

Rohit Reddy Chananagari Prabhakar

The application of Artificial Intelligence (AI) in educational analytics has ushered in unprecedented enhancement in student learning prediction, learning at scale, auto-grading, and institution-level decision-making. However, the increased generation and processing of student information precipitate unprecedented concerns in privacy and security, spanning breaches and inference attacks through adversarial manipulations, unauthorized third-party information extraction, and AI model explainability restrictions. In this article, we provide a critical overview of privacy-preserving AI-based educational analytics databases, from state-of-the-art approaches such as Differential Privacy (DP), Federated Learning (FL), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Blockchain. Global regulation compliance regimes such as the General Data Protection Regulation (GDPR), the Family Educational Rights and Privacy Act (FERPA), and the California Consumer Privacy Act (CCPA) are reviewed, with the ethical trade-offs and conflicts between utility and privacy preservation laid bare. Projected future directions from Zero-Knowledge Proofs (ZKP) and decentralized AI platforms through hybrid AI-privacy architecture and explainable AI (XAI) are discussed.

Anders Dalskov, Daniel Escudero, Ariel Nof

No abstract is available for this record.

B. Pabitha, V. Vani, Shridhar Sanshi, N. Karthik

No abstract is available for this record.

Gioia Arnone

No abstract is available for this record.

Jing Wang, Xue Yuan, Yingjie Xu, Yudi Zhang

Large language models (LLMs) have brought significant advancements to artificial intelligence, particularly in understanding and generating human language. However, concerns over management burden and data security have grown alongside their capabilities. To solve the problem, we design a blockchain‐based distributed LLM framework, where LLM works in the distributed mode and its outputs can be stored and verified on a blockchain to ensure integrity, transparency, and traceability. In addition, a multiparty signature‐based authentication mechanism is necessary to ensure stakeholder consensus before publication. To address these requirements, we propose a threshold elliptic curve digital signature algorithm that counters malicious adversaries in environments with three or more participants. Our approach relies on discrete logarithmic zero‐knowledge proofs and Feldman verifiable secret sharing, reducing complexity by forgoing multiplication triple protocols. When compared with some related schemes, this optimization speeds up both the key generation and signing phases with constant rounds while maintaining security against malicious adversaries.

Aniket Chanda, Mir Junaid Rasool

No abstract is available for this record.

Saurav Bhattacharya, Madhavi Najana, Harsh Gupta, Anoop Gupta

This article explores the importance of putting users at the center of consent processes, in Single Sign On (SSO) systems to tackle privacy issues and empower user independence. It dives into the world of SSO systems shedding light on their privacy weaknesses and the need for users to have control over how their data is shared. By looking at privacy focused SSO solutions and their drawbacks the article suggests a plan to give users control over their data sharing preferences during authentication. The main elements of this plan include a user consent management interface, consent choices, educational materials, preference persistence and tracking logs. Additionally it talks about the obstacles in implementing consent driven SSO systems like creating consent APIs and incorporating privacy boosting technologies such as zero knowledge proofs and decentralized identity frameworks. By tackling these hurdles and promoting designs that prioritize users the article aims to help create authentication solutions that prioritize privacy in line, with changing regulations and user desires.

Andrea Basso, Mingjie Chen, Tako Boris Fouotsa, Péter Kutas · 7 authors

No abstract is available for this record.

Emanuele Scala, Leonardo Mostarda

No abstract is available for this record.

Romain Gay, Bogdan Ursu

No abstract is available for this record.