Privacy-Preserving Public Information for Sequential Games

Avrim Blum; Jamie Morgenstern; Ankit Sharma; Adam Smith

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arXiv · 1402.4488

Privacy-Preserving Public Information for Sequential Games

Avrim Blum·Jamie Morgenstern·Ankit Sharma·Adam Smith

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Abstract

In settings with incomplete information, players can find it difficult to coordinate to find states with good social welfare. For example, in financial settings, if a collection of financial firms have limited information about each other's strategies, some large number of them may choose the same high-risk investment in hopes of high returns. While this might be acceptable in some cases, the economy can be hurt badly if many firms make investments in the same risky market segment and it fails. One reason why many firms might end up choosing the same segment is that they do not have information about other firms' investments (imperfect information may lead to `bad' game states). Directly reporting all players' investments, however, raises confidentiality concerns for both individuals and institutions. In this paper, we explore whether information about the game-state can be publicly announced in a manner that maintains the privacy of the actions of the players, and still suffices to deter players from reaching bad game-states. We show that in many games of interest, it is possible for players to avoid these bad states with the help of privacy-preserving, publicly-announced information. We model behavior of players in this imperfect information setting in two ways -- greedy and undominated strategic behaviours, and we prove guarantees on social welfare that certain kinds of privacy-preserving information can help attain. Furthermore, we design a counter with improved privacy guarantees under continual observation.

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BibTeX RIS

Avrim Blum, Jamie Morgenstern, Ankit Sharma, Adam Smith. 2014-02-18. Privacy-Preserving Public Information for Sequential Games. https://arxiv.org/abs/1402.4488

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