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Targeted Homomorphic Attribute-Based Encryption

机译:基于目标同态属性的加密

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In (key-policy) attribute-based encryption (ABE), messages are encrypted respective to attributes x, and keys are generated respective to policy functions f. The ciphertext is decryptable by a key only if f(x) = 0. Adding homomorphic capabilities to ABE is a long standing open problem, with current techniques only allowing compact homomorphic evaluation on ciphertext respective to the same x. Recent advances in the study of multi-key FHE also allow cross-attribute homomorphism with ciphertext size growing (quadratically) with the number of input ciphertexts. We present an ABE scheme where homomorphic operations can be performed compactly across attributes. Of course, decrypting the resulting ciphertext needs to be done with a key respective to a policy f with f(x_i) = 0 for all attributes involved in the computation. In our scheme, the target policy f needs to be known to the evaluator, we call this targeted homomorphism. Our scheme is secure under the polynomial hardness of learning with errors (LWE) with sub-exponential modulus-to-noise ratio. We present a second scheme where there needs not be a single target policy. Instead, the decryptor only needs a set of keys representing policies f_j s.t. for any attribute x_i there exists f_j with f_j(x_i) - 0. In this scheme, the ciphertext size grows (quadratically) with the size of the set of policies (and is still independent of the number of inputs or attributes). Again, the target set of policies needs to be known at evaluation time. This latter scheme is secure in the random oracle model under the polynomial hardness of LWE with sub-exponential noise ratio.
机译:在(密钥策略)基于属性的加密(ABE)中,消息分别针对属性x进行加密,并且密钥针对策略功能f进行生成。仅当f(x)= 0时,密文才可通过密钥解密。向ABE添加同态能力是一个长期存在的开放问题,而当前的技术仅允许对与同一个x相同的密文进行紧凑的同态评估。多密钥FHE研究的最新进展还允许随着输入密文数量的增加,密文的交叉属性同构性(按平方)增长。我们提出了一种ABE方案,其中同构运算可以跨属性紧凑地执行。当然,对于涉及计算的所有属性,需要使用对应于策略f的密钥对所得密文进行解密,其中f(x_i)= 0。在我们的方案中,评估者需要了解目标策略f,我们称其为目标同态。我们的方案在具有次指数模数噪声比的带误差学习(LWE)的多项式硬度下是安全的。我们提出了第二种方案,其中不需要单个目标策略。相反,解密器仅需要一组代表策略f_j s.t的密钥。对于任何属性x_i,都存在具有f_j(x_i)-0的f_j。在此方案中,密文大小随策略集的大小而(平方地)增长(并且仍独立于输入或属性的数量)。同样,需要在评估时知道目标策略集。在随机预言模型中,在具有次指数噪声比的LWE的多项式硬度下,后一种方案是安全的。

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