Collaborative Research: CIF: Medium: Harnessing Intrinsic Dynamics for Inherently Privacy-preserving Decentralized Optimization

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Recent advances in communication and networking technologies lead to the emergence and proliferation of distributed interconnected systems such as swarm robotics, sensor networks, smart-grid, the Internet of Things, and collaborative machine-learning systems. A task that is fundamental to the operation of these systems is decentralized optimization, where participating nodes cooperate to minimize an overall objective function that is the sum (or average) of individual nodes’ local objective functions. Moreover, since individual nodes’ local objective functions may bear sensitive information of local nodes such as medical records in collaborative learning and user energy-consumption profiles in a smart grid, in many cases, the decentralized optimization algorithm has to make sure that a participant’s sensitive information is protected from being inferable by other participating nodes or external eavesdroppers. Although plenty of results have been proposed for decentralized optimization, most of these results do not consider the problem of privacy protection. Conventional information-technology privacy mechanisms are inappropriate for decentralized optimization because they either have to compromise the accuracy of optimization (in, e.g., differential-privacy-based approaches) or incur heavy extra computation/communication overhead (in, e.g., cryptography-based privacy approaches). The lack of effective privacy solutions for decentralized optimization not only severely hinders the social adoption of new technologies, but also leads to potential vulnerabilities since stealing private information is usually the basis for sophisticated cybersecurity attacks. Leveraging the iterative properties of decentralized optimization algorithms, the project aims to establish a new privacy-preserving approach for decentralized optimization that neither compromises optimization accuracy nor incurs large computation/communication overhead. Combined with the additional merit of needing no assistance of a trusted central coordinator, the proposed approach is expected to transformatively advance privacy-preservation in networked systems and make impacts in many applications ranging from connected vehicles, swarm robotics, smart grid, sensor networks, to collaborative machine learning. Leveraging control theory, this project seeks to establish methodologies and associated theories for inherently privacy-preserving decentralized optimization by exploiting the intrinsic dynamical properties of decentralized optimization. Besides maintaining optimization accuracy, the dynamics-based privacy approach is also free of encryption, which not only guarantees limited extra computation/communication overhead, but also promises a decentralized implementation without the assistance of any trusted third party or data aggregator. The main research thrusts are to: 1) Develop a privacy framework for dynamical systems that explicitly considers the iterative evolution of information in decentralized optimization; 2) Design perturbations to dynamics that enable privacy without affecting the accuracy of decentralized optimization methods for convex problems, and quantify the effects of the perturbations on convergence speed; 3) Investigate the influence of privacy design on decentralized non-convex optimization and exploit freedom in privacy design to facilitate decentralized non-convex problems; and 4) Evaluate the results using experiments on a multi-robot platform and connected vehicles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.