Protecting Your Online Persona: A Preferential Selective Encryption Approach for Enhanced Privacy in Tweets, Images, Memes, and Metadata

The proliferation of online social networking websites has resulted in the growth of online communities and increased communication among users. However, sharing personal information can pose a privacy threat to users, leaving them vulnerable to attacks like blackmail and fraud. Also, with the rise in scandals such as Geofeedia and Cambridge Analytica, as well as Facebook’s admission of sharing user data with top mobile phone manufacturers, worries about privacy on social networking platforms have become increasingly palpable. This paper discusses the privacy risks of sharing personal information on social networking websites. The proposed approaches offer a comprehensive protection of user privacy and prevents unauthorized access to user’s data and information by social network providers and unauthorized users. The study employs a preferential selective encryption approach that encompasses not only tweets but also other personally identifiable components, including images, memes, and metadata. This approach serves as a comprehensive solution to encrypt all elements within a social network, making it a one-stop destination for privacy protection in online social networks (OSN). When compared to other investigated schemes, the proposed approach significantly reduces privacy leakage to 0.48 units while achieving a high utility score of 0.8116 units for non-sensitive attributes in tweets. Moreover, it demonstrates remarkable performance in maintaining sensitive information coverage with notably minimal information loss: 0.4 units for images, 0.3 units for memes, and 12.7 units for metadata. Thus, in essence these findings, backed by quantifiable measures of privacy and utility, strongly validate the effectiveness of the proposed methods in achieving a superior balance between privacy preservation and utility when compared to its contemporaries, particularly in applications like online social networks (OSNs). Furthermore, the suggested method’s architecture makes it platform-neutral; it can be customized for any social networking website, such as Facebook, Instagram, and LinkedIn, and it may include any attribute that the user chooses to keep private.