TR2017-015

On Methods for Privacy-Preserving Energy Disaggregation


    •  Wang, Y., Raval, N.J., Ishwar, P., Hattori, M., Hirano, T., Matsuda, N., Shimizu, R., "On Methods for Privacy-Preserving Energy Disaggregation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 2017.
      BibTeX TR2017-015 PDF
      • @inproceedings{Wang2017mar2,
      • author = {Wang, Ye and Raval, Nisarg J and Ishwar, Prakash and Hattori, Mitsuhiro and Hirano, Takato and Matsuda, Nori and Shimizu, Rina},
      • title = {On Methods for Privacy-Preserving Energy Disaggregation},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2017,
      • month = mar,
      • url = {https://www.merl.com/publications/TR2017-015}
      • }
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  • Research Area:

    Information Security

Abstract:

Household energy monitoring via smart-meters motivates the problem of disaggregating the total energy usage signal into the component energy usage and operating patterns of individual appliances. While energy disaggregation enables useful analytics, it also raises privacy concerns because sensitive household information may also be revealed. Our goal is to preserve analytical utility while mitigating privacy concerns by processing the total energy usage signal. We consider processing methods that attempt to remove the contribution of a set of sensitive appliances from the total energy signal. We show that while a simple model-based approach is effective against an adversary making the same model assumptions, it is much less effective against a stronger adversary employing neural networks in an inference attack. We also investigate the performance of employing neural networks to estimate and remove the energy usage of sensitive appliances. The experiments used the publicly available UK-DALE dataset that was collected from actual households.

 

  • Related News & Events

    •  NEWS   MERL to present 10 papers at ICASSP 2017
      Date: March 5, 2017 - March 9, 2017
      Where: New Orleans
      MERL Contacts: Petros T. Boufounos; Takaaki Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Anthony Vetro; Ye Wang
      Research Areas: Computer Vision, Computational Sensing, Digital Video, Information Security, Speech & Audio
      Brief
      • MERL researchers will presented 10 papers at the upcoming IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), to be held in New Orleans from March 5-9, 2017. Topics to be presented include recent advances in speech recognition and audio processing; graph signal processing; computational imaging; and privacy-preserving data analysis.

        ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 2000 participants each year.
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