| On August 20,2021,the "Personal Information Protection Law of the People’s Republic of China" voted at the 30 th session of the 13 th National People’s Congress Standing Committee was considered by many media to be a powerful weapon to regulate the personalized pricing behavior of platform operators,so how to apply the law has become the focus of public attention.This is the purpose of this paper,in addition to the introduction and conclusion,the whole paper is divided into four chapters and their contents are as follows:The first chapter is the legal definition of personalized pricing behavior,which mainly discusses the concept of personalized pricing behavior and its legal nature in the big data environment.The second chapter deals with the current situation and existing problems in judicial practice of personalized pricing practices,which are embodied in the difficulties in determining infringement,difficulties in presenting evidence by consumers,and the long-term lack of a special system for public interest litigation for personal information protection.The third chapter is an analysis of the legal regulation of personalized pricing behavior from the perspective of the Personal Information Protection Law.It was found that there are still problems in determining the illegality of personalized pricing behavior,applying the public interest litigation system for personal information protection,and allocating the burden of proof for consumers and platform operators.The fourth chapter is a suggestion for improving the legal regulation of personalized pricing behavior from the perspective of the Personal Information Protection Law,and in view of the problem analysis of the previous chapter,it puts forward suggestions such as clarifying the "major impact" standard in article24,expanding the "notification-consent" framework,clarifying the definition criteria for "many individuals" in article 70,refining the eligible subjects for initiating personal information public interest litigation,and reasonably applying the inversion of the burden of proof of causality,so as to regulate personalized pricing behavior well. |