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Privacy-Preserving HMM Forward Computation

In many areas such as bioinformatics, pattern recognition, and signal processing, Hidden Markov Models (HMMs) have become an indispensable statistical tool. A fundamental building block for these applications is the Forward algorithm which computes the likelihood to observe a given sequence of emissions for a given HMM. The classical Forward algorithm requires that one party holds both the model and observation sequences. However, we observe for many emerging applications and services that the models and observation sequences are held by different parties who are not able to share their information due to applicable data protection legislation or due to concerns over intellectual property and privacy. This renders the application of HMMs infeasible. In this paper, we show how to resolve this evident conflict of interests using secure two-party computation. Concretely, we propose Priward which enables two mutually untrusting parties to compute the Forward algorithm securely, i.e., without requiring either party to share her sensitive inputs with the other or any third party. The evaluation of our implementation of Priward shows that our solution is efficient, accurate, and outperforms related works by a factor of 4 to 126. To highlight the applicability of our approach in real-world deployments, we combine Priward with the widely used HMMER biosequence analysis framework and show how to analyze real genome sequences in a privacy-preserving manner.