高效的用户中心、隐私友好且灵活的可穿戴数据聚合与共享
Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing
AI总结：
针对可穿戴数据共享中的隐私风险，提出SAMA方案，结合多密钥部分同态加密和属性加密，实现高效、用户中心的灵活数据聚合与细粒度共享。
摘要：
可穿戴设备可以为个人和公众提供服务。然而，由云提供商收集的可穿戴数据可能带来隐私风险。为了在保持全部功能的同时降低这些风险，医疗系统需要支持隐私友好型数据处理和共享的解决方案，以满足三种主要使用场景：（i）数据所有者请求处理自己的数据，以及多个数据请求者请求处理（ii）单个或（iii）多个数据所有者的数据。现有工作缺乏数据所有者的访问控制，并且不能高效支持这些场景，使其不适合可穿戴设备。为了解决这些限制，我们提出了一种新颖、高效、用户中心、隐私友好且灵活的数据聚合与共享方案，名为SAMA。SAMA使用多密钥部分同态加密方案，以灵活地支持来自单个或多个数据所有者的数据聚合，同时在处理过程中保护隐私。它还使用基于密文策略的属性加密方案，以支持基于用户中心访问控制的与多个数据请求者的细粒度共享。形式化安全分析表明，SAMA支持数据机密性和授权。SAMA还在计算和通信开销方面进行了分析。我们的实验结果表明，SAMA比相关的最先进解决方案更高效地支持隐私保护的灵活数据聚合。
英文摘要：
Wearable devices can offer services to individuals and the public. However, wearable data collected by cloud providers may pose privacy risks. To reduce these risks while maintaining full functionality, healthcare systems require solutions for privacy-friendly data processing and sharing that can accommodate three main use cases: (i) data owners requesting processing of their own data, and multiple data requesters requesting data processing of (ii) a single or (iii) multiple data owners. Existing work lacks data owner access control and does not efficiently support these cases, making them unsuitable for wearable devices. To address these limitations, we propose a novel, efficient, user-centric, privacy-friendly, and flexible data aggregation and sharing scheme, named SAMA. SAMA uses a multi-key partial homomorphic encryption scheme to allow flexibility in accommodating the aggregation of data originating from a single or multiple data owners while preserving privacy during the processing. It also uses ciphertext-policy attribute-based encryption scheme to support fine-grain sharing with multiple data requesters based on user-centric access control. Formal security analysis shows that SAMA supports data confidentiality and authorisation. SAMA has also been analysed in terms of computational and communication overheads. Our experimental results demonstrate that SAMA supports privacy-preserving flexible data aggregation more efficiently than the relevant state-of-the-art solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
