Privacy Preserving Systems With Crowd Blending
Over the years, the Internet has become a platform where individuals share their thoughts and personal information. In some cases, these content contain some damaging or sensitive information, which a malicious data collector can leverage to exploit the individual. Nevertheless, what people consider to be sensitive is a relative matter: it not only varies from one person to another but also changes through time. Therefore, it is hard to identify what content is considered sensitive or damaging, from the viewpoint of a malicious entity that does not target specific individuals, rather scavenges the data-sharing platforms to identify sensitive information as a whole. However, the actions that users take to change their privacy preferences or hide their information assists these malicious entities in discovering the sensitive content.
This thesis offers Crowd Blending techniques to create privacy-preserving systems while maintaining platform utility. In particular, we focus on two privacy tasks for two different data-sharing platforms— i) concealing content deletion on social media platforms and ii) concealing censored information in cryptocurrency blockchains. For the concealment of the content deletion problem, first, we survey the users of social platforms to understand their deletion privacy expectations. Second, based on the users’ needs, we propose two new privacy-preserving deletion mechanisms for the next generation of social platforms. Finally, we compare the effectiveness and usefulness of the proposed mechanisms with the current deployed ones through a user study survey. For the second problem of concealing censored information in cryptocurrencies, we present a provably secure stenography scheme using cryptocurrencies. We show the possibility of hiding censored information among transactions of cryptocurrencies.
- Doctor of Philosophy
- Computer Science
- West Lafayette