PCS-ADS: Privacy Computing System for Agricultural Data Security

With the wide application of Internet of Things, big data and other technologies in smart agriculture, more and more agricultural data are collected and used. The current analysis and processing through IoT platforms and other centralised methods have more serious data security problems. Privacy computing is a technology that completes data security calculations under the premise that both parties do not disclose data, and the data of each participant participates in the calculation is in the form of cipher text to maximally reduce the risk of data leakage. Aiming at the small volume and high frequency scenario of agricultural data, this paper proposes a distributed privacy computing framework by integrating cryptography, edge computing and blockchain, and designs a privacy computing system called PCS-ADS for smart agricultural data. This paper focuses on describing the system model architecture and system function implementation, which includes key business processes, core modules, core algorithms and other parts. Finally, the security and performance evaluation of the system is carried out. The results show that, for sum and variance, the computation time for multi-party under the amount of 1w data of each party is no more than 0.1s, and for the high-frequency computation scenarios, the computation time under the amount of 100 data of each party is no more than 7s and 1.5s, respectively. Experimental results prove that PCS-ADS is effective and has a strong practicality.