Privacy Games
Yiling Chen, Or Sheffet, Salil Vadhan

TL;DR
This paper explores how explicit privacy incentives influence agent behavior in simple strategic games, revealing conditions under which privacy concerns lead to randomized or deterministic strategies, differing from hardwired privacy models.
Contribution
It introduces a new model where privacy is incentivized through payments, analyzing how this affects agent strategies and demonstrating cases where behavior aligns with or diverges from traditional privacy-preserving mechanisms.
Findings
Privacy incentives can lead to deterministic strategies.
Some payment schemes induce behavior consistent with Randomized Response.
Behavior differs significantly from models with hardwired privacy concerns.
Abstract
The problem of analyzing the effect of privacy concerns on the behavior of selfish utility-maximizing agents has received much attention lately. Privacy concerns are often modeled by altering the utility functions of agents to consider also their privacy loss. Such privacy aware agents prefer to take a randomized strategy even in very simple games in which non-privacy aware agents play pure strategies. In some cases, the behavior of privacy aware agents follows the framework of Randomized Response, a well-known mechanism that preserves differential privacy. Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent A, in an attempt to discover the secret type of agent B, offers B a gift that one type of B agent likes and the other type dislikes. As opposed to previous works, B's…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsExperimental Behavioral Economics Studies · Survey Sampling and Estimation Techniques · Privacy-Preserving Technologies in Data
