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Privacy-preserving probabilistic inference based on hidden markov models

机译:基于隐马尔可夫模型的隐私保护概率推理

摘要

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.
机译:服务器根据存储在客户端的观察序列来确定隐马尔可夫模型(HMM)的参数,其中客户端具有加法同态密码系统的解密密钥和加密密钥,而服务器仅具有加密密钥。服务器初始化HMM的参数并迭代更新参数,直到当前迭代的观察序列的概率与先前迭代的观察序列的概率之间的差值高于阈值为止,其中,对于每次迭代,参数基于给定观察序列和HMM的参数的每对状态的加密条件联合概率来更新ID,其中,加密条件概率是使用服务器和客户端之间的安全多方计算(SMC)在加密域中确定的。

著录项

  • 公开/公告号US8433893B2

    专利类型

  • 公开/公告日2013-04-30

    原文格式PDF

  • 申请/专利权人 WEI SUN;SHANTANU RANE;

    申请/专利号US201113076418

  • 发明设计人 WEI SUN;SHANTANU RANE;

    申请日2011-03-30

  • 分类号H04L29/06;

  • 国家 US

  • 入库时间 2022-08-21 16:43:32

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