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Hidden Markov model (HMM) has been well studied and extensively used. In thispaper, we present DPHMM ({Differentially Private Hidden Markov Model}), an HMMembedded with a private data release mechanism, in which the privacy of thedata is protected through a graph. Specifically, we treat every state in Markovmodel as a node, and use a graph to represent the privacy policy, in which"indistinguishability" between states is denoted by edges between nodes. Due tothe temporal correlations in Markov model, we show that the graph may bereduced to a subgraph with disconnected nodes, which become unprotected andmight be exposed. To detect such privacy risk, we define sensitivity hull anddegree of protection based on the graph to capture the condition of informationexposure. Then to tackle the detected exposure, we study how to build anoptimal graph based on the existing graph. We also implement and evaluate theDPHMM on real-world datasets, showing that privacy and utility can be bettertuned with customized policy graph.

DPHMM: Customizable Data Release with Differential Privacy via Hidden Markov Model