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Internet of things (IoT) devices are becoming increasingly popular thanks tomany new services and applications they offer. However, in addition to theirmany benefits, they raise privacy concerns since they share fine-grainedtime-series user data with untrusted third parties. In this work, we study theprivacy-utility trade-off (PUT) in time-series data sharing. Existingapproaches to PUT mainly focus on a single data point; however, temporalcorrelations in time-series data introduce new challenges. Methods thatpreserve the privacy for the current time may leak significant amount ofinformation at the trace level as the adversary can exploit temporalcorrelations in a trace. We consider sharing the distorted version of a user'strue data sequence with an untrusted third party. We measure the privacyleakage by the mutual information between the user's true data sequence andshared version. We consider both the instantaneous and average distortionbetween the two sequences, under a given distortion measure, as the utilityloss metric. To tackle the history-dependent mutual information minimization,we reformulate the problem as a Markov decision process (MDP), and solve itusing asynchronous actor-critic deep reinforcement learning (RL). We evaluatethe performance of the proposed solution in location trace privacy on bothsynthetic and GeoLife GPS trajectory datasets. For the latter, we show thevalidity of our solution by testing the privacy of the released locationtrajectory against an adversary network.

Privacy-Aware Time-Series Data Sharing with Deep Reinforcement Learning