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More than half of the world’s population benefits from Online Social Networks’(OSN s) services. A considerable part of these services is mainly based on applying analytics on user data to infer their preferences and enrich their experience accordingly. At the same time, user data is monetized by service providers to run their business models. Therefore, providers tend to extensively collect (personal) data about users. However, this data is oftentimes used for various purposes without informed consent of the users. Providers share this data in different forms with third parties (eg, data brokers). Moreover, user sensitive data was repeatedly a subject of unauthorized access by malicious parties. These issues have demonstrated the insufficient commitment of providers to user privacy, and consequently, raised users’ concerns. Despite the emergence of privacy regulations (eg, GDPR and CCPA), recent studies showed that user personal data collection and sharing sensitive data are still continuously increasing.

A number of privacy-friendly OSN s have been proposed to enhance user privacy by reducing the need for central service providers. However, this improvement in privacy protection usually comes at the cost of losing social connectivity and many analytics-based services of the wide-spread OSN s. This dissertation addresses this issue by first proposing an approach to privacy-friendly OSN s that maintains established social connections. Second, approaches that allow users to collaboratively apply distributed analytics while preserving their privacy are presented. Finally, the dissertation contributes to better assessment and mitigation of the risks …