Privacy-Preserving Probabilistic Inference Based on Hidden Markov Models

• US 20120254605A1
• Filed: 03/30/2011
• Published: 10/04/2012
• Est. Priority Date: 03/30/2011
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Sun, Wei, Rane, Shantanu
• Granted Patent: US 8,433,893 B2
• Time in Patent Office: Days
• US Class Current: 713/150
• CPC Class Codes: G09C 1/00 Apparatus or methods whereby a given sequence of signs, e.g. an intelligible text, is transformed into an unintelligible sequence of signs by transposing the signs or groups of signs or by replacing them by others according to a predetermined system cryptographic typewriters G09C3/00
• CPC Class Codes: H04L 2209/42 Anonymization, e.g. involving pseudonyms
• CPC Class Codes: H04L 9/00 Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
• CPC Class Codes: H04L 9/008 involving homomorphic encryption

Abstract

Parameters of a hidden Markov model (HMM) are determined by a server based on an observation sequence stored at a client, wherein the client has a decryption key and an encryption key of an additively homomorphic cryptosystem, and the server has only the encryption key. The server initializes parameters of the HMM and updates the parameters iteratively until a difference between a probability of the observation sequence of a current iteration and a probability of the observation sequence of a previous iteration is above a threshold, wherein, for each iteration, the parameters are updated based on an encrypted conditional joint probability of each pair of states given the observation sequence and the parameters of the HMM, wherein the encrypted conditional probability is determining in an encrypted domain using a secure multiparty computation (SMC) between the server and the client.

Claims