Cryptography and Security (cs.CR)

Abstract: In a traditional cloud storage system, users benefit from the convenience it provides but also take the risk of certain security and privacy issues. To ensure confidentiality while maintaining data sharing capabilities, the Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme can be used to achieve fine-grained access control in cloud services. However, existing approaches are impaired by three critical concerns: illegal authorization, key disclosure, and privacy leakage. To address these, we propose a blockchain-based data governance system that employs blockchain technology and attribute-based encryption to prevent privacy leakage and credential misuse. First, our ABE encryption system can handle multi-authority use cases while protecting identity privacy and hiding access policy, which also protects data sharing against corrupt authorities. Second, applying the Advanced Encryption Standard (AES) for data encryption makes the whole system efficient and responsive to real-world conditions. Furthermore, the encrypted data is stored in a decentralized storage system such as IPFS, which does not rely on any centralized service provider and is, therefore, resilient against single-point failures. Third, illegal authorization activity can be readily identified through the logged on-chain data. Besides the system design, we also provide security proofs to demonstrate the robustness of the proposed system.

Abstract: Continuous authentication has been widely studied to provide high security and usability for mobile devices by continuously monitoring and authenticating users. Recent studies adopt multibiometric fusion for continuous authentication to provide high accuracy even when some of captured biometric data are of a low quality. However, existing continuous fusion approaches are resource-heavy as they rely on all classifiers being activated all the time and may not be suitable for mobile devices. In this paper, we propose a new approach to multibiometric continuous authentication: two-dimensional dynamic fusion. Our key insight is that multibiometric continuous authentication calculates two-dimensional matching scores over classifiers and over time. Based on this, we dynamically select a set of classifiers based on the context in which authentication is taking place, and fuse matching scores by multi-classifier fusion and multi-sample fusion. Through experimental evaluation, we show that our approach provides a better balance between resource usage and accuracy than the existing fusion methods. In particular, we show that our approach provides higher accuracy than the existing methods with the same number of score calculations by adopting multi-sample fusion.