Assessing the Effectiveness of Masking and Encryption in Safeguarding the Identity of Social Media Publishers from Advanced Metadata Analysis

Abstract

Machine learning algorithms, such as KNN, SVM, MLP, RF, and MLR, are used to extract valuable information from shared digital data on social media platforms through their APIs in an effort to identify anonymous publishers or online users. This can leave these anonymous publishers vulnerable to privacy-related attacks, as identifying information can be revealed. Twitter is an example of such a platform where identifying anonymous users/publishers is made possible by using machine learning techniques. To provide these anonymous users with stronger protection, we have examined the effectiveness of these techniques when critical fields in the metadata are masked or encrypted using tweets (text and images) from Twitter. Our results show that SVM achieved the highest accuracy rate of 95.81% without using data masking or encryption, while SVM achieved the highest identity recognition rate of 50.24% when using data masking and AES encryption algorithm. This indicates that data masking and encryption of metadata of tweets (text and images) can provide promising protection for the anonymity of users’ identities.