Privacy Preservation for Time Series Data in the Electricity Sector

The big data era has raised public concern regarding private information leakage. Therefore, in the electricity sector, many classical privacy preserving mechanisms based on noise injection have been designed and implemented for meter data. However, injected noise of large magnitudes can affect the statistical structure of these data. Therefore, in this study, we identify the inherent randomness embedded in time series data to mitigate this issue. To this end, we study the potential of using this inherent randomness to protect the privacy for both high and low resolution time series data. We propose a privacy preserving mechanism using stochastic differential equation modeling. We theoretically prove the effectiveness of our proposed framework and design several methods to implement our mechanism to aid various data-driven consumer behavior analysis tasks in the electricity sector. The numerical results indicate that our framework can simultaneously maintain the desired level of privacy preservation and value of data in practice.

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