Data Desensitization And Usability Research In The Internet Of Things Environment

With the rapid development of the Internet of Things(IoT)technology,IoT devices and applications need to collect,process,transmit,and store a large amount of user data to provide personalized services.However,these processes also increase the risk of data privacy leaks.Sensitive data leaks can have serious consequences,such as severe threats to personal privacy,business secrets,and public safety.Therefore,the security and reliability of data privacy protection technology in IoT security are crucial issues for ensuring IoT data security.Data anonymization,as an important IoT data privacy protection technology,is being increasingly applied in IoT security.In the processing of some sensitive data,anonymization technology can effectively protect data privacy to avoid data leaks and misuse.However,data anonymization changes the original format of data,which affects the availability of data,reduces the effectiveness and accuracy of data,and may even lead to incorrect decisions.Therefore,balancing the privacy and availability of data anonymization has become a hot topic in the field of data protection.To address the issue of the effectiveness of anonymized data,this paper proposes a data anonymization system for multiple scenarios,which aims to solve the availability problem during the data anonymization process.The main principle is to quantitatively analyze the privacy and availability of anonymized data output by various anonymization techniques and select the anonymization strategy that best fits the application scenario requirements through decision tree technology,thereby maximizing the availability of the original data.This paper mainly analyzes various data anonymization techniques such as confusion,permutation,k-anonymity,l-diversity,t-closeness,and differential privacy,and forms an evaluation framework that analyzes the privacy protection attributes and availability of these techniques.The framework can adaptively select anonymization techniques according to the data characteristics and application scenarios to improve the availability and accuracy of data.In addition,the system can provide multiple secure anonymization solutions according to the user’s privacy requirements to ensure the data privacy.The proposed data anonymization system for multiple scenarios can effectively balance the privacy and availability of data anonymization.Data experimental results show that the system can maximize the availability of original data while ensuring data privacy,and has practical application value.