在移动云计算中实现高效分散和保护隐私的数据共享

Jiawei Zhang, Ning Lu, Teng Li, Jianfeng Ma
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引用次数: 0

摘要

移动云计算(MCC)近年来发展迅速,能够为智能移动设备普及的云用户提供数据外包和共享服务。虽然这些服务带来了各种便利,但也带来了非法访问、用户隐私泄露等安全隐患。为了保护云数据共享的安全性,防止未经授权的访问,许多研究使用基于密文策略属性的加密(CP-ABE)进行细粒度访问控制。然而,在资源有限的移动设备上同时支持细粒度访问控制、大型大学、无密钥托管和MCC中的隐私保护,并具有表达性访问策略、高效率、可验证性和可推诿性的实用和安全的数据共享方案尚未得到充分探索。为此,本文提出了一种高效、多权威的大宇宙策略隐藏数据共享(EMA-LUPHDS)方案。在该方案中,我们在访问策略中采用了完全隐藏策略来保护用户的隐私。为了适应大规模分布式MCC环境,我们对多权威CP-ABE进行了优化,使其能够兼容大属性域。同时,为了提高效率,我们的方案利用了具有可推诿性的在线/离线可验证外包解密技术。最后,我们通过广泛的性能评估证明了我们的建议在MCC中实现数据共享的灵活性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing
Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.
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