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ESE Ph.D. Thesis Defense: “Cryptographic Foundations for Control and Optimization”
April 22, 2021 at 9:00 AM - 11:00 AM
Abstract: Advances in communication technologies and computational power have determined a technological shift in the data paradigm. The resulting architecture requires sensors to send local data to the cloud for global processing such as estimation, control, decision and learning, leading to both performance improvement and privacy concerns. This thesis explores the emerging field of private control for Internet of Things, where it bridges dynamical systems and computations on encrypted data, using applied cryptography and information-theoretic tools.
Our research contributions are privacy-preserving interactive protocols for cloud-outsourced decisions and data processing, as well as for aggregation over networks in multi-agent systems, both of which are essential in control theory and machine learning. In these settings, we guarantee privacy of the data providers’ local inputs over multiple time steps, as well as privacy of the cloud service provider’s proprietary information. Specifically, we focus on (i) private solutions to cloud-based constrained quadratic optimization problems from distributed private data; (ii) oblivious distributed weighted sum aggregation; (iii) linear and nonlinear cloud-based control on encrypted data; (iv) private evaluation of cloud-outsourced data-driven control policies with sparsity and low-complexity requirements. In these scenarios, we require computational privacy and stipulate that each participant is allowed to learn nothing more than its own result of the computation. Our protocols employ homomorphic encryption schemes and secure multi-party computation tools with the purpose of performing computations directly on encrypted data, such that leakage of private information at the computing entity is minimized. To this end, we co-design solutions with respect to both control performance and privacy specifications, and we streamline their implementation by exploiting the rich structure of the underlying private data.
Advisor: George J. Pappas, UPS Foundation Professor and Chair of the Department of Electrical and Systems Engineering
Chair: Manfred Morari, Practice Professor, Department of Electrical and Systems Engineering
Member: Tal Rabin, Professor, Department of Computer and Information Science
Member: Sebastian Angel, Raj and Neera Singh Term Assistant Professor, Department of Computer and Information Science
ESE Ph.D. Candidate
Bio: Andreea B. Alexandru received the B.Eng. degree from the Department of Automatic Control and Systems Engineering, “Politehnica” University of Bucharest, Romania, in 2015. She is a Ph.D. candidate in the Department of Electrical and Systems Engineering, University of Pennsylvania, advised by George J. Pappas. Her main research interests are applied cryptography and the privacy and security of control systems and machine learning. In 2019, she received an NSF iREDEFINE professional development award and was selected in the Rising Stars in Electrical Engineering and Computer Sciences workshop.