Secure Computing with Privacy Preservation for Cyber-Physical Systems



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Cyber-physical systems (CPS) often referred as "next generation of engineered systems" are sensing and communication systems that offer tight integration of computation and networking capabilities to monitor and control entities in the physical world. The advent of the cloud and service computing models has contributed significantly to the growth of CPS allowing massively parallel computations. Wireless computing devices and smart phones are also being integrated with the physical components for building efficient CPS.

Though CPS have in filtrated into many areas due to their advantages, security and privacy are major considerations for building efficient and high-confidence CPS. Many domains of CPS such as smart metering, sensor/data aggregation, crowd sensing, traffic control etc., typically collect huge amounts of individual information for data analysis and decision making; therefore privacy is a serious concern in CPS. For example, the operation of smart grids relies on information continuously provided by and about their users. The collection of information helps the system make smart decisions through sophisticated machine learning algorithms. However, privacy breaches during any stage of the system can be an undesirable loss of privacy for the participants, thereby putting their promised benefits at risk.

This dissertation focuses on addressing the privacy issues in certain CPS. Due to its importance, CPS and its communication networks inevitably become the targets of attackers and malicious users either during data collection, transmission, or computation. Most of the traditional approaches protect the privacy of individuals' data by employing trusted third parties or entities for data collection and computation. An important challenge in these large-scale distributed applications is how to protect the privacy of the participants during computation and decision making, especially when such third party entities are untrusted.

Considering various CPS applications involving modeling, we discuss our approaches utilizing applied cryptographic techniques and differential privacy for privacy preserving secure computation. Since confidential information must not be inappropriately released, and the use of untrusted information must not corrupt trusted computation and the utility. This work concludes by highlighting on the development of such tools for state-of-the-art applications considering application-specific requirements.



Privacy Preservation, Paillier Cryptosystem, Differential Privacy