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Privacy protection probabilistic inference based on hidden Markov model

机译:基于隐马尔可夫模型的隐私保护概率推理

摘要

Parameter Hidden Markov Model (HMM) is asked by the server based on the observed value series to be stored at the client, the client has the encryption key and decryption key additive homomorphism encryption system, the server only the encryption key have. The server initializes the parameters of HMM, the difference between the probability of the observed value series of iterations prior probability of the observed value series of iterations until the current exceeds a threshold value, update iteratively the parameters, repetitive each time it is, the conditional probability when the parameters of the HMM and observation sequence is given, is updated based on the conditional joint probability that is encrypted for each pair of states, the encrypted parameter to the server is obtained in the domain is encrypted using secure multiparty computation between the client and the (SMC).
机译:服务器根据观察值序列要求参数隐马尔可夫模型(HMM)存储在客户端,客户端具有加密密钥和解密密钥加法同态加密系统,服务器只有加密密钥具有。服务器初始化HMM的参数,直到当前值超过阈值为止,迭代观测值系列的概率与迭代观测值系列的概率之间的差,迭代更新参数,每次都重复,有条件的基于为每个状态对加密的条件联合概率来更新给定HMM和观察序列参数的概率,使用客户端之间的安全多方计算对域中获得的服务器加密参数进行加密和(SMC)。

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