Paper 2020/806

Toward Comparable Homomorphic Encryption for Crowd-sensing Network

Daxin Huang, Qingqing Gan, Xiaoming Wang, Chengpeng Huang, and Yijian Lin

Abstract

As a popular paradigm, crowd-sensing network emerges to achieve sensory data collection and task allocation to mobile users. On one hand these sensory data could be private and sensitive, and on the other hand, data transmission separately could incur heavy communication overhead. Fortunately, the technique of homomorphic encryption (HE) allows the addictive and/or multiplicative operations over the encrypted data as well as privacy protection. Therefore, several data aggregation schemes based on HE are proposed for crowd-sensing network. However, most of the existing schemes do not support ciphertext comparison efficiently, thus data center cannot process ciphertexts with flexibility. To address this challenge, we propose a comparable homomorphic encryption (CompHE) scheme based on Lagrange’s interpolation theorem, which enables ciphertext comparison between multiple users in crowdsensing network. Based on the Partial Discrete Logarithm and Decisional Diffie-Hellman assumption, the proposed CompHE scheme is provably secure in the random oracle model. Performance analysis confirms that the proposed scheme have improved the computational efficiency compared with existing schemes.