Abstract
A growing number of large-scale monitoring and control systems enabling a more intelligent infrastructure, such as smart grids and intelligent transportation systems, rely for their operation on private data obtained from individuals or from privacy-sensitive entities more generally. This entry discusses how to capture and enforce formal quantitative privacy constraints to design estimation and control algorithms that can trade off performance for the level of privacy offered. A state-of-the art definition of privacy called differential privacy, which provides guarantees against adversaries with arbitrary side information, is presented. It is shown that tools from systems and control can be used to enforce differential privacy guarantees when publishing real-time statistics or solving distributed optimization and control problems based on sensitive data.
Keywords
- Data privacy
- Differential privacy
- Stochastic filtering
- Networked systems
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Le Ny, J. (2021). Privacy in Network Systems. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_100139-1
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_100139-1
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