Visible to the public PMDA: Privacy-Preserving Multi-Functional Data Aggregation Without TTP in Smart Grid

TitlePMDA: Privacy-Preserving Multi-Functional Data Aggregation Without TTP in Smart Grid
Publication TypeConference Paper
Year of Publication2018
AuthorsHe, Z., Pan, S., Lin, D.
Conference Name2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)
Date Publishedaug
Keywordscomposability, cryptography, data aggregation, data privacy, electricity readings, Energy management, Human Behavior, Meters, Metrics, Multi-functional computation, multifunctional aggregation, PMDA, power engineering computing, privacy, privacy flaws, privacy leakage, privacy preserving, privacy protection, privacy-preserving aggregation, privacy-preserving multifunctional data aggregation, pubcrawl, security, security analysis, Smart grid, Smart Grid Privacy, Smart grids, smart meters, smart power grids, trusted third party, TTP
Abstract

In the smart grid, residents' electricity usage needs to be periodically measured and reported for the purpose of better energy management. At the same time, real-time collection of residents' electricity consumption may unfavorably incur privacy leakage, which has motivated the research on privacy-preserving aggregation of electricity readings. Most previous studies either rely on a trusted third party (TTP) or suffer from expensive computation. In this paper, we first reveal the privacy flaws of a very recent scheme pursing privacy preservation without relying on the TTP. By presenting concrete attacks, we show that this scheme has failed to meet the design goals. Then, for better privacy protection, we construct a new scheme called PMDA, which utilizes Shamir's secret sharing to allow smart meters to negotiate aggregation parameters in the absence of a TTP. Using only lightweight cryptography, PMDA efficiently supports multi-functional aggregation of the electricity readings, and simultaneously preserves residents' privacy. Theoretical analysis is provided with regard to PMDA's security and efficiency. Moreover, experimental data obtained from a prototype indicates that our proposal is efficient and feasible for practical deployment.

URLhttps://ieeexplore.ieee.org/document/8456023
DOI10.1109/TrustCom/BigDataSE.2018.00154
Citation Keyhe_pmda:_2018