As part of this thesis, we aim to strengthen the protection of personal data in distributed environments, focusing on the identification and management of new inference channels. We will begin by conducting a comprehensive state-of-the-art review to catalog current issues related to data security in these environments. Next, we plan to define a workflow that incorporates these new inference channels into detection systems, detailing their discovery and integration.

We also intend to propose a generalized model capable of integrating different types of inference channels while preserving data availability and semantics. Finally, we aim to develop a dynamic mechanism for adjusting security policies based on usage observation and adaptation to identified risks.

All of these efforts are intended to provide adaptive and effective solutions for the protection of personal data in the face of evolving threats in distributed systems.