Mohammed Mahmoud Said Dawoud

• In intelligent and autonomous systems, users must often share private data with external platforms to enable advanced functionalities. A key challenge in this process arises when the data to be protected is not a single, precise value, but is instead inherently uncertain or represents a range of possible values. This thesis addresses the privacy of such uncertain data, which is formally represented and processed using set-based methods. We review existing privacy-preserving techniques and introduce new mechanisms to safeguard this data during processing on untrusted platforms, exploring both cryptographic and non-cryptographic approaches. Specifically, the thesis presents novel privacy-preserving mechanisms for set-based data processing, categorized by the data type within the sets. For sets of real-valued data, we introduce a differential privacy mechanism for set-based estimation in linear and non-linear dynamical systems. This approach protects the sensitive information contained within sets, such as zonotopes that model system uncertainties, while minimizing the loss of utility for the estimation process. For sets of discrete, binary data, we propose a mechanism that uses Fast Fully Homomorphic Encryption to ensure privacy. This method allows for the secure processing of sets represented as logical zonotopes in untrusted environments, maintaining computational practicality. The evaluations demonstrate the effectiveness and practicality of the proposed mechanisms across various applications in autonomous and intelligent systems.