Massive multidimensional health data collected from Internet of Things (IoT) devices are driving a new era of smart health, and with it come privacy concerns. Privacy-preserving data aggregation (PDA) is a proven solution providing statistics while hiding raw data. However, existing PDA schemes ignore the willingness of data owners to share, so data owners may refuse to share data. To increase their willingness to contribute data, we propose an OPtional dimEnsional pRivacy-preserving data Aggregation scheme (OPERA) to provide data contributors with options on sharing dimensions while keeping their choices and data private. OPERA uses selection vectors to represent the decisions of users and count participants dimensionally and achieves data privacy and utility based on a multisecret sharing method and symmetric homomorphic cryptography. Analyses show that in OPERA, the probability of adversaries breaching privacy is less than 4.68e-97. Performance evaluations demonstrate that OPERA is outstanding in computation and practical in communication.

中文翻译：

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OPERA：智能医疗系统的可选维度隐私保护数据聚合


从物联网 (IoT) 设备收集的海量多维健康数据正在推动智能健康的新时代，随之而来的是隐私问题。隐私保护数据聚合 (PDA) 是一种经过验证的解决方案，可在隐藏原始数据的同时提供统计数据。然而，现有的PDA方案忽视了数据所有者共享的意愿，因此数据所有者可能会拒绝共享数据。为了提高他们贡献数据的意愿，我们提出了一种可选的维度隐私保护数据聚合方案（OPERA），为数据贡献者提供共享维度的选项，同时保持他们的选择和数据的私密性。 OPERA使用选择向量来表示用户的决策并对参与者进行维度计数，并基于多重秘密共享方法和对称同态密码学实现数据隐私和实用性。分析表明，在OPERA中，对手侵犯隐私的概率小于4.68e-97。性能评估表明，OPERA具有出色的计算能力和实用的通信能力。