This paper develops a data collection and processing framework that achieves individual users’ data privacy and the operator’s information accuracy simultaneously. Data privacy is enhanced by adding noise and applying quantization to the data before transmission, and the privacy of an individual user is measured by information-theoretic analysis. This paper develops a data recovery and clustering method for the operator to extract features from the privacy-preserving, partially corrupted, and partially observed measurements of a large number of users. To prevent cyber intruders from accessing the data of many users, it also develops a decentralized algorithm such that multiple data owners can collaboratively recover and cluster the data without sharing the raw measurements directly. The recovery accuracy is characterized analytically and showed to be close to the fundamental limit of any recovery method. The proposed algorithm is proved to converge to a critical point from any initial point. The method is evaluated on recorded Irish smart meter data and UMass smart microgrid data.

本文开发了一种数据收集和处理框架，可以同时实现个人用户的数据隐私和运营商的信息准确性。通过添加噪声并在传输之前对数据进行量化来增强数据隐私，并且通过信息理论分析来测量单个用户的隐私。本文开发了一种数据恢复和聚类方法，供运营商从大量用户的隐私保护，部分破坏和部分观察的测量中提取特征。为了防止网络入侵者访问许多用户的数据，它还开发了一种分散算法，以便多个数据所有者可以协作地恢复和群集数据，而无需直接共享原始度量。通过分析对恢复精度进行了表征，结果表明该精度接近任何恢复方法的基本极限。实践证明，所提出的算法可以从任何初始点收敛到临界点。根据记录的爱尔兰智能电表数据和UMass智能微电网数据评估该方法。