Abstract
Internet of Thing (IoT) systems usually have less storage and computing power than desktop systems. This paper proposes an efficient BGV-type homomorphic encryption scheme in order fit for secure computing on IoT system. Our scheme reduces the storage space for switch keys and ciphertext evaluation time comparing with previous BGV-type cryptosystems. Specifically, the switch key in homomorphic computations can be a constant but no longer one for each level. Moreover, the product of two ciphertexts can be at the same sublayer as them and the multiplication operations can be repeated between two sublayers. As a result, the multiplication times will not be limited by L in an L-level circuit and, thus, the ciphertext evaluation time will decrease significantly. We implement the scheme with the C language. The performance test shows that the efficiency of the improved scheme is better than Helib in same configurations.
Highlights
The Internet of Thing (IoT) systems have been widely used in people’s daily life [1,2]
In a breakthrough work [12], Gentry demonstrated that fully homomorphic encryption was theoretically possible based on ideal lattices
KeyGen: given the public parameters, first, the key generation algorithm selects a random vector s as the secret key. It computes b = −( a·s +p·e) mod q L−1, where a is a random element in Rq L−1, e is sampled from χ, and p is the plaintext modulu, and let ( a, b) be the public key
Summary
The Internet of Thing (IoT) systems have been widely used in people’s daily life [1,2]. Homomorphic encryption allows a device to perform arbitrary computations on encrypted data without user secret key. Following Gentry’s work, many researchers tried to improve the performance of homomorphic encryption. (BGV) dramatically improved the performance of the BV-type homomorphic encryption. Gentry and Halevi and Smart reduced the size of public keys and ciphertexts [18] of the BGV-type scheme at the cost of increasing the probability of key recovery attack. They optimized the execution times of fast Fourier transform (FFT) and Chinese reminder theorem (CRT) by reducing the.
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