关键词: 相关性差分隐私, 隐私发布, 智能电表, 周期敏感度, 序列分解

Abstract:

To solve the problems of high global sensitivity, complex calculation of query on electricity consumption data and that independent noise can be easily filtered, Period Sensitivity Method（PSM） is proposed. PSM decomposes the consumption series into stationary series set and active series set according to the data statistical characteristics. According to the data sparsity and correlation, two disparate privacy protection strategies are put forward. It adds the independent identically distributed noise to the stationary series, then the stationary series with noise passes through the smoothing filter to obtain better utility. It adds the correlated noise to the active series?to resist the filtering attack, whose auto-correlation function is similar to active consumption series. Theoretical analysis and experimental evaluation show that PSM satisfies differential privacy and has better utility and smaller computational complexity.

Key words: correlated differential privacy, privacy release, smart meter, period sensitivity, series decomposition