Enhancing user privacy by preprocessing distributed smart meter data

The increasing presence of renewable sources requires power grid operators to continuously monitor electricity generation and demand in order to maintain the grid's stability. To this end, smart meters have been deployed to collect realtime information about the current grid load and forward it to the utility in a timely manner. High resolution smart meter data can however reveal the nature of appliances and their mode of operation with high accuracy, and thus endanger user privacy. In this paper, we investigate the impact on user privacy when the consumption data collected by distributed smart metering devices are preprocessed prior to their usage. We therefore assess the impact on the successful classification of appliances when sensor readings are (1) quantized, (2) down-sampled at a lower sampling rate, and (3) averaged by means of an FIR filter. Our evaluation shows that a combination of these preprocessing steps can provide a balanced trade-off that is in the interests of both users (privacy protection) and utilities (near real-time information).