Abstract
Many applications involve networks of interacting agents, each of whom is interested in making an individual inference or decision, the performance of which can be enhanced by the exchange of information with other agents. Though such agents can clearly benefit from exchanging information, they may also wish to maintain a degree of privacy in that information exchange. Such situations give rise to a notion of competitive privacy, which can be explored through a combination of information theory and game theory. In particular, information theory can be used to characterize the trade-off between privacy of data and the usefulness of that data for an individual agent, while game theory can be used to model the interactions between multiple agents each of whom is mindful of that trade-off. These ideas are explored in this chapter, first in a general setting, and then particularly in the context of data exchange for distributed state estimation, in which specific solutions can be obtained. Interesting open issues and other potential applications will also be discussed. Much of this abstract was originally used as the abstract of the author’s academic keynote address to the Workshop on Advances in Network Localization and Navigation in London in 2015 (http://anln.spsc.tugraz.at/Program2015).
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Acknowledgements
This work was prepared while the author was visiting Stanford University under the support of the Precourt Institute for Energy. The support of the U. S. National Science Foundation under Grants ECCS-1549881 and ECCS-1647198 is also gratefully acknowledged.
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Poor, H.V. (2018). Privacy in Networks of Interacting Agents. In: Tempo, R., Yurkovich, S., Misra, P. (eds) Emerging Applications of Control and Systems Theory. Lecture Notes in Control and Information Sciences - Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-67068-3_19
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