Abstract
Artificial Internet of Things (AIoT) is that the system collects all kinds of information in real-time through various sensors, and intelligence analysis of the data through machine learning in the terminal equipment, edge domains, or cloud centers, including positioning, comparison, forecasting, scheduling, etc. which brings about the data security and privacy issues. The blockchain is a tamper-evident, unforgeable distributed ledger that protects security and privacy through the famous algorithm zk-SNARK, which is also widely used in virtual digital currencies such as Zcash. In addition, by using zk-SNARK technology in the Loopring DEX 3.0 in Ethereum, not only decentralization but also transaction performance can be guaranteed. However, there are three main problems of zk-SNARK, one is the need to guarantee calculation accuracy, two is the long time to generate evidence, especially when using Lagrangian interpolation to QAP the transaction data requires more computation; the last is the poor scalability, especially when nodes need to recalculate all data when adding new transactions. In this paper, we propose a modified zk-SNARK based on Newtonian interpolation, improve the QAP part of zk-SNARK by Newtonian interpolation, and verify the correctness of the scheme through instantiation. Finally, we analyze the computational efficiency of the two interpolation methods, and the results show that Newton interpolation solves the above two problems in the original zk-SNARK, and significantly reduces the time complexity of the algorithm, which can further promote the application of blockchain in data management of AIoT.