Privacy Preserving Probabilistic Inference With Hidden Markov Models
Alice possesses a sample of private data from which she wishes to obtain some probabilistic inference. Bob possesses Hidden Markov Models (HMMs) for this purpose, but a person wants the model parameters to remain private. This paper develops a framework that enables Alice and Bob to collaboratively compute the so-called forward algorithm for HMMs while satisfying their privacy constraints. This is achieved using a public-key additively homomorphic cryptosystem. The authors' framework is asymmetric in the sense that a larger computational overhead is incurred by Bob who has higher computational resources at his disposal, compared with Alice who has limited computing resources.