Legal Protection of Artificial Intelligence Data and Algorithms from the Perspective of Internet of Things Resource Sharing

Abstract

There are few laws and regulations related to privacy protection in the existing artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects my country’s current weak protection of big data privacy. In order to solve the problem of sharing artificial intelligence data and algorithms, it becomes very important to study the legal protection of artificial intelligence data and algorithms from the perspective of Internet of Things resource sharing. This article is aimed at studying the use of laws to protect artificial intelligence data and algorithms. Aiming at reducing the bullwhip effect, a most effective bullwhip effect model derivation algorithm is proposed. This method can not only share customer demand information with members at all levels in the supply chain but also achieve information sharing among members at all levels. Calculate the proportion of the overall time of the program through multiple statistical data ( $m=30$ , $k=12$ ; and $m=60$ , $k=15$ ), and extract two special values representing the overall situation ( $m=30$ , $k=12$ ; $m=60$ , $k=15$ ). Most of the time consumption of this program is concentrated in the secret distribution stage, accounting for about 80% on average.