Achieving Privacy-Friendly Storage and Secure Statistics for Smart Meter Data on Outsourced Clouds
Zijian Zhang, Mianxiong Dong, Liehuang Zhu, Zhitao Guan, Ruoyu Chen, Rixin Xu, Kaoru Ota · IEEE Transactions on Cloud Computing · 2017
Smart meters have already been widely used for electric utilities to provide reliable power service. Since those meters keep reporting customer's energy consumption data in minute-level or even second-level, Terabyte-level big data has to be stored and analyzed for the companies. To relieve the storage and computation pressure, some companies attempt to outsource their data on the cloud. However, this exposes customer's privacy at risk, because customer's activities can be inferred from analyzing the meter readings. In this paper, we propose a privacy-friendly cloud storage (PCS) scheme and three secure cloud statistic (SCS) schemes for smart meter data on outsourced clouds. Putting these schemes together achieves three queries from the electric companies. Next, we provably analyze the privacy and the security for these schemes. Finally, we design MapReduce algorithms to show the performance for the cloud statistic.