@article{859fb5f250134b618298e6314e404f07,
title = "Modeling and Analysis of Leaky Deception Using Signaling Games with Evidence",
abstract = "Deception plays critical roles in economics and technology, especially in emerging interactions in cyberspace. Holistic models of deception are needed in order to analyze interactions and to design mechanisms that improve them. Game theory provides such models. In particular, existing work models deception using signaling games. But signaling games inherently model deception that is undetectable. In this paper, we extend signaling games by including a detector that gives off probabilistic warnings when the sender acts deceptively. Then, we derive pooling and partially separating equilibria of the game. We find that: 1) high quality detectors eliminate some pure-strategy equilibria; 2) detectors with high true-positive rates encourage more honest signaling than detectors with low false-positive rates; 3) receivers obtain optimal outcomes for equal-error-rate detectors; and 4) surprisingly, deceptive senders sometimes benefit from highly accurate deception detectors. We illustrate these results with an application to defensive deception for network security. Our results provide a quantitative and rigorous analysis of the fundamental aspects of detectable deception.",
keywords = "Deception, game theory, signaling game, strategic communication, trust management",
author = "Jeffrey Pawlick and Edward Colbert and Quanyan Zhu",
note = "Funding Information: This work was supported in part by an NSF IGERT Grant through the Center for Interdisciplinary Studies in Security and Privacy (CRISSP) at New York University, in part by the National Science Foundation (NSF) under Grant CNS-1544782, Grant EFRI-1441140, and Grant SES-1541164, in part by the Department of Energy under Grant DE-NE0008571, and in part by the Army Research Laboratory and was accomplished under Cooperative Agreement W911NF-17-2-0104. Funding Information: Manuscript received February 12, 2018; revised July 12, 2018; accepted December 2, 2018. Date of publication December 12, 2018; date of current version April 10, 2019. This work was supported in part by an NSF IGERT Grant through the Center for Interdisciplinary Studies in Security and Privacy (CRISSP) at New York University, in part by the National Science Foundation (NSF) under Grant CNS-1544782, Grant EFRI-1441140, and Grant SES-1541164, in part by the Department of Energy under Grant DE-NE0008571, and in part by the Army Research Laboratory and was accomplished under Cooperative Agreement W911NF-17-2-0104. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. An earlier version of this work was presented at the 2018 Workshop on the Economics of Information Security [28]. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Walid Saad. (Corresponding author: Jeffrey Pawlick.) J. Pawlick and Q. Zhu are with the Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201 USA (e-mail: jpawlick@nyu.edu; quanyan.zhu@nyu.edu). Publisher Copyright: {\textcopyright} 2018 IEEE.",
year = "2019",
month = jul,
doi = "10.1109/TIFS.2018.2886472",
language = "English (US)",
volume = "14",
pages = "1871--1886",
journal = "IEEE Transactions on Information Forensics and Security",
issn = "1556-6013",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "7",
}