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.

中文翻译：

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在移动云计算中实现高效的分散和隐私保护数据共享

移动云计算（MCC）近来飞速发展，能够为拥有无处不在的智能移动设备的云用户提供数据外包和共享服务。这些服务虽然带来了各种便利，但也带来了诸多安全隐患，如非法访问、用户隐私泄露等。为了保护云数据共享的安全性免受未经授权的访问，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、无密钥托管和隐私保护的实用且安全的数据共享方案，并具有表达访问策略、高效率、可验证性和可免责性尚未充分探索。因此，我们调查了挑战并提出了一种高效且多机构的大型宇宙策略隐藏数据共享（EMA-LUPHDS）方案。在该方案中，我们采用完全隐藏的策略来保护访问策略中的用户隐私。为了适应大规模和分布式的 MCC 环境，我们优化了多权限 CP-ABE 以兼容大属性宇宙。同时，为了提高效率，我们的方案利用了在线/离线和可验证的外包解密技术。最后，我们通过广泛的性能评估证明了我们在 MCC 中数据共享的建议的灵活性和高效率。我们采用完全隐藏的策略来保护访问策略中的用户隐私。为了适应大规模和分布式的 MCC 环境，我们优化了多权限 CP-ABE 以兼容大属性宇宙。同时，为了提高效率，我们的方案利用了在线/离线和可验证的外包解密技术。最后，我们通过广泛的性能评估证明了我们在 MCC 中数据共享的建议的灵活性和高效率。我们采用完全隐藏的策略来保护访问策略中的用户隐私。为了适应大规模和分布式的 MCC 环境，我们优化了多权限 CP-ABE 以兼容大属性宇宙。同时，为了提高效率，我们的方案利用了在线/离线和可验证的外包解密技术。最后，我们通过广泛的性能评估证明了我们在 MCC 中数据共享的建议的灵活性和高效率。