Towards an analytical framework for privacy-preserving aggregation in smart grid

Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer ScienceRecent changes to the power grid are expected to influence the way energy is provided and consumed by customers. Advanced Metering Infrastructure (AMI) is a tool to incorporate these changes for modernizing the electricity grid. However, this information-based power grid can reveal sensitive private information from the user's perspective as it can gather highly-granular power consumption data. This has led to limited consumer acceptance and proliferation of the smart grid. Hence, it is crucial to design a mechanism to prevent the leakage of such sensitive consumer usage information. Among different solutions for preserving consumer privacy in Smart Grid Networks (SGN), private data aggregation techniques have received a tremendous focus from security researches. In this work, a novel and efficient CDMA-based approach to privacy-preserving aggregation in SGNs, utilizing random perturbation of power consumption data, with limited use of traditional cryptography has been presented. The efficiency and performance of the proposed privacy-preserving data aggregation scheme is evaluated and validated through extensive statistical analyses and simulations. In the past few years, only limited work has been done on quantifying the privacy leakage of the smart grid due to the deployment of the smart meters. The goal of such quantification is to provide a formal framework to show how much privacy is lost in smart metering systems and to what extent the proposed solutions reduce this loss of privacy. As a second research direction, we study the existing metrics for quantifying privacy in various domains. Then, we present four information theoretic metrics to represent the privacy gained by utilizing different Smart grid Privacy Preserving Mechanisms (SPPMs). We investigate the applicability of the theory of information entropy as a potential privacy metric and suggest using conditional entropy, joint entropy, and relative entropy to further analyze the privacy-leakage in smart metering systems