Privacy preserving IoT-based crowd-sensing network with comparable homomorphic encryption and its application in...

IoT-based crowd-sensing network, which aims to achieve data collection and task allocation to mobile users, become more and more popular in recent years. This data collected by IoT devices may be private and directly transmission of these data maybe incur privacy leakage. With the help of homomorphic encryption (HE), which supports the additive and/or multiplicative operations over the encrypted data, privacy preserving crowd-sensing network is now possible. Until now several such secure data aggregation schemes based on HE have been proposed. In many cases, ciphertext comparison is an important step for further secure data processing. However efficient ciphertext comparison is not supported by most such schemes. In this paper, aiming at enabling ciphertext comparison among multiple users in crowd-sensing network, with Lagrange's interpolation technique we propose comparable homomorphic encryption (CompHE) schemes. We also prove our schemes' security, and the performance analysis show our schemes are practical. We also discuss the applications of our IoT based crowd-sensing network with comparable homomorphic encryption for combatting COVID19, including the first example of privacy preserving close contact determination based on the spatial distance, and the second example of privacy preserving social distance controlling based on the spatial difference of lockdown zones, controlled zones and precautionary zones. From the analysis we see our IoT based crowd-sensing network can be used for contact tracing without worrying about the privacy leakage. Compared with the existing CompHE schemes, our proposals can be collusion resistance or secure in the semi-honest model while the previous schemes cannot achieve this easily. Our schemes only need 4 or 5 modular exponentiation when implementing the most important comparison algorithm, which are better than the existing closely related scheme with advantage of 50% or 37.5%.