论文标题

有效以用户为中心的隐私友好且灵活的可穿戴数据聚合和共享

Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing

论文作者

Jastaniah, Khlood, Zhang, Ning, Mustafa, Mustafa A.

论文摘要

可穿戴设备可以为个人和公众提供服务。但是,云提供商收集的可穿戴数据可能会带来隐私风险。为了降低这些风险的同时保持全部功能,医疗保健系统需要解决方案,以便可以容纳三种主要用例的隐私友好数据处理和共享:(i)数据所有者请求处理自己的数据的数据所有者,以及多个数据请求者要求(ii)单个或(ii)多个数据所有者进行数据处理。现有工作缺乏数据所有者访问控制,并且不能有效地支持这些情况,从而使它们不适合可穿戴设备。为了解决这些限制,我们提出了一种新颖,高效,以用户为中心,对隐私友好且灵活的数据聚合和共享方案,名为SAMA。 SAMA使用多键的部分同型加密方案,以允许灵活性来容纳来自单个或多个数据所有者的数据的汇总,同时在处理过程中保留隐私。它还使用基于Ciphertext-Policy属性的加密方案来支持基于以用户为中心的访问控制的多个数据请求者的细颗粒共享。正式的安全性分析表明,SAMA支持数据机密性和授权。 SAMA还通过计算和通信开销进行了分析。我们的实验结果表明,SAMA比相关的最新解决方案更有效地支持保护隐私的灵活数据聚合。

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.

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