Visible to the public A Privacy Preserving Model for Energy Internet Base on Differential Privacy

TitleA Privacy Preserving Model for Energy Internet Base on Differential Privacy
Publication TypeConference Paper
Year of Publication2017
AuthorsCao, H., Liu, S., Zhao, R., Gu, H., Bao, J., Zhu, L.
Conference Name2017 IEEE International Conference on Energy Internet (ICEI)
ISBN Number978-1-5090-5759-7
KeywordsAlgorithm design and analysis, Batteries, composability, data availability, data mining, data privacy, data protection, Data security, Differential privacy, electrical data use analysis, energy internet, energy Internet privacy protection, Exponential distribution, Human Behavior, Load management, Monitoring, NILM, Non-intrusive Load Monitoring, nonintrusive load monitoring, power engineering computing, privacy, privacy preserving, privacy preserving data mining, privacy preserving data release, privacy preserving model, pubcrawl, Resiliency, Scalability, Sensitivity, user behavior privacy

Comparing with the traditional grid, energy internet will collect data widely and connect more broader. The analysis of electrical data use of Non-intrusive Load Monitoring (NILM) can infer user behavior privacy. Consideration both data security and availability is a problem must be addressed. Due to its rigid and provable privacy guarantee, Differential Privacy has proverbially reached and applied to privacy preserving data release and data mining. Because of its high sensitivity, increases the noise directly will led to data unavailable. In this paper, we propose a differentially private mechanism to protect energy internet privacy. Our focus is the aggregated data be released by data owner after added noise in disaggregated data. The theoretically proves and experiments show that our scheme can achieve the purpose of privacy-preserving and data availability.

Citation Keycao_privacy_2017