Differential Privacy for Deep Learning-based Online Energy Disaggregation System. / Zhang, Xiaoyu; Kuenzel, Stefanie.

The 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) . IEEE, 2020. p. 1-5.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Forthcoming

Documents

  • Accepted Manuscript

    Accepted author manuscript, 433 KB, PDF document

Abstract

Online energy disaggregation is an advanced technology that can help both consumers and the utility to implement load components analysis, enhancing the reliability of demand-side management. However, the online system requires continuous access to personal electricity data to train an online machine learning model, which would infer personal information. In this paper, we introduce a privacy-preserving online energy disaggregation system, the online model and training data can be protected by adding Gaussian noise to the model during the training process.
Original languageEnglish
Title of host publicationThe 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)
PublisherIEEE
Pages1-5
Number of pages5
Publication statusAccepted/In press - 20 Apr 2020
EventThe 2020 IEEE PES Innovative Smart Grid Technologies Europe Conference - The Hague, Netherlands
Duration: 25 Oct 2020 → …

Conference

ConferenceThe 2020 IEEE PES Innovative Smart Grid Technologies Europe Conference
Abbreviated titleISGT-Europe
CountryNetherlands
CityThe Hague
Period25/10/20 → …

ID: 37887925