摘要
全同态加密在云计算安全领域具有重要应用价值。公钥尺寸较大是现有全同态加密体制普遍存在的缺点。为解决这一问题,文章将基于身份加密的思想和全同态加密体制相结合,利用近似特征向量方法,无需生成运算密钥,构造了一种真正意义上基于身份的全同态加密体制。采用更有效的陷门生成算法,将文献[13]中基于身份的全同态加密的体制参数由m≥5nlogq减小至m≈2nlogq。本体制的安全性在随机喻示模型下归约到容错学习问题难解性。
Fully homomorphic encryption is of great value in cloud computing. The public key of the existing fully homomorphic encryption has generally oversized. Using the approximate eigenvector method and taking the advantages of no evaluate keys, this paper constructs an identity-based fully homomorphic eneryption which compromises the merits of both kinds of encryption. Using the new effective trapdoor generation algorithm, the parameter m ≥ 5 nlogq in paper [ 13 ] has reduced to m 2nlogq. In the random oracle model, the security of the scheme strictly reduces to the hardness of decisional learning with error problems.
出处
《信息工程大学学报》
2015年第3期267-273,共7页
Journal of Information Engineering University
基金
河南省科技创新杰出青年基金资助项目(134100510002)
河南省基础与前沿技术研究项目(142300410002)
关键词
全同态加密
基于身份加密
近似特征向量
容错学习问题
前象可采样陷门单向函数
fully homomorphic encryption
identity-based encryption
approximate eigenvector
learning with error
trapdoor one-way function with preimage sampling