@conference {1525278,
	title = {To Warn or Not to Warn: Online Signaling in Audit Games},
	booktitle = {AI for Social Good Workshop},
	year = {2020},
	abstract = {
	In health care organizations, a patient{\textquoteright}s privacy is threatened by the misuse of their electronic health record (EHR). To monitor privacy intrusions, logging systems are often deployed to trigger alerts whenever a suspicious access is detected. However, such mechanisms are insufficient in the face of small budgets, strategic attackers, and large false positive rates. In an attempt to resolve these problems, EHR systems are increasingly incorporating signaling, so that whenever a suspicious access request occurs, the system can, in real time, warn the user that the access may be audited. This gives rise to an online problem in which one needs to determine 1) whether a warning should be triggered and 2) the likelihood that the data request will be audited later. In this paper, we formalize this auditing problem as a Signaling Audit Game (SAG). A series of experiments with 10 million real access events (containing over 26K alerts) from Vanderbilt University Medical Center (VUMC) demonstrate that a strategic presentation of warnings adds value in that SAGs realize significantly higher utility for the auditor than systems without signaling.



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},
	author = {Chao Yan and Haifeng Xu and Vorobeychik, Yevgeniy and Bo Li and Daniel Fabbri and Bradley A. Malin}
}
