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Improvement on a Fuzzy Identity-based Encryption Scheme

机译:基于模糊身份的加密方案的改进

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—Sahai and Waters introduced a fuzzy id-based encryption (FIBE) scheme in which an identity was viewed as a set of descriptive attributes, and any user with a private key for an identity ID was allowed to decrypt a ciphertext encrypted with an identity ID′ , if and only if the identity ID and ID′ were close to each other as measured by the “set overlap” distance metric. After that, Shweta Agrawal, Xavier Boyen, Vinod Vaikuntanathan, Panagiotis Voulgaris and Hoeteck Weeto constructed a fuzzy id-based encryption scheme from the hardness of the Learning With Errors ( LWE ) problem. The two schemes both had more longer public and private keys, and it was not very efficient. This paper gives more efficient fuzzy id-based encryption scheme from lattice, in which the public keys are greatly reduced, and there are two vectors corresponding to each bit of the identity by using of the algorithm Sampleleft introduced by Shweta Agrawal, Dan Boneh and Xavier Boyen, and only a trapdoor basis is the secret key. Furthermore, the scheme is dealing with N − bit information and is also secure from the hardness of the Learning With Errors ( LWE ) problem, and all these greatly raise efficiency.
机译:-Sahai和Waters介绍了一种基于模糊的ID的加密(FIBE)方案,其中将身份视为一组描述性属性,并且允许使用具有身份ID的私钥的任何用户解密以具有身份ID加密的密文',如果且仅当通过“设置重叠”距离度量测量时彼此彼此接近彼此。之后,Shweta Agrawal,Xavier Boyen,Vinod Vaikuntanathan,Panagiotis Voulgaris和Hoeteck Weeto建造了一种模糊的ID基加密方案,从学习的硬度(LWE)问题。这两种方案都有更长的公共和私钥,并不是很有效。本文提供了从格子的更高效的模糊基础的加密方案,其中公共密钥大大减少,并且通过使用Shweta Agrawal,Dan Boneh和Xavier引入的算法SampleTeft,存在对应于身份的每位的两个向量Boyen,只有一个陷入困境的基础是秘密密钥。此外,该方案正在处理N比特信息,并且还可以从学习的硬度(LWE)问题,并且所有这些都大大提高效率。

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