Towards an Activity-aware Pufferfish Framework for Local Privacy of Household SmartWater Meter Data

Data from smart water meters can provide valuable insights but it also exposes sensitive information about the behaviour of individuals. This short paper considers local privacy protection for allowing households to selectively hide secrets about their behaviours, as well as sharing their data for analysis. A single water meter signal can be disaggregated into activity channels, with each channel reporting the times and volume of specific activities such as garden irrigation or absences. Adversaries are assumed to have common knowledge of the distinctive distributions of such activities including their likely times of day, temporal- and auto-correlations, and seasonal patterns. Pufferfish privacy is a general purpose framework for secrets to be protected, with mechanisms for protecting those secrets given an adversary's prior knowledge of the generating mechanisms for the data. In this short paper we propose a Pufferfish activity-aware privacy protection framework for water meter data. Using a real-world dataset of hourly water meter readings for 3557 households, we demonstrate how to hide secrets about the times of leaks, absences and garden irrigation whilst preserving utility for accurate analysis of the data of individuals and populations.