Temporal Differential Privacy for Human Activity Recognition

Abstract

Differential privacy (DP) is a method to protect individual privacy when the data is used for downstream analytical tasks. The core ability of DP to quantify privacy numerically separates it from other privacy-preserving methods. In human activity recognition (HAR), differential privacy can protect users' privacy who contribute their data to train machine learning algorithms. While some methods are developed for privacy protection in such cases, no method quantifies privacy and seamlessly integrates into machine learning frameworks like DP. The paper proposes a DP framework called TEMPDIFF (short for temporal differential privacy), which guarantees privacy preserving human activity recognition for wearable time-series data with competitive classification performance and works with any machinelearning/deep-learning methods. TEMPDIFF capitalizes on the temporal characteristics of wearable sensor data to improve the modelling task, which enhances the privacy-utility tradeoff. TEMPDIFF uses ensembling and a novel temporal partitioning algorithm for time-series data to ensure optimal training of ensemble models. In TEMPDIFF, consensus through ensembling and the addition of controlled Laplacian noise obscures sensitive information used to train the models, guaranteeing strict levels of differential privacy. The proposed method is evaluated on two popular HAR datasets. It outperforms the classification accuracy and privacy budget for both datasets compared to the state-ofthe-art approaches.