Deception through Half-Truths

Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politically motivated "leaks" and fake news about candidates.Typical considerations of deception view it as providing false information.However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked.We consider the problem of how much an adversary can affect a principal's decision by "half-truths", that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. The principal's problem can be modeled as one of predicting future states of variables in a dynamic Bayes network, and we show that, while theoretically the principal's decisions can be made arbitrarily bad, the optimal attack is NP-hard to approximate, even under strong assumptions favoring the attacker. However, we also describe an important special case where the dependency of future states on past states is additive, in which we can efficiently compute an approximately optimal attack. Moreover, in networks with a linear transition function we can solve the problem optimally in polynomial time.

[1]  Viliam Lisý,et al.  Game-Theoretic Foundations for the Strategic Use of Honeypots in Network Security , 2015, Cyber Warfare.

[2]  Daniel Grosu,et al.  A Game Theoretic Investigation of Deception in Network Security , 2009, ICCCN.

[3]  Irwin Greenberg The Role of Deception in Decision Theory , 1982 .

[4]  Vincent Conitzer,et al.  Signaling in Bayesian Stackelberg Games , 2016, AAMAS.

[5]  Atul Prakash,et al.  Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[6]  Michael P. Wellman,et al.  A Cloaking Mechanism to Mitigate Market Manipulation , 2018, IJCAI.

[7]  Haifeng Xu,et al.  Algorithmic Bayesian persuasion , 2015, STOC.

[8]  Zhuoshu Li,et al.  Revenue Enhancement via Asymmetric Signaling in Interdependent-Value Auctions , 2019, AAAI.

[9]  Haifeng Xu,et al.  Information Disclosure as a Means to Security , 2015, AAMAS.

[10]  Lujo Bauer,et al.  Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.

[11]  Fred Cohen,et al.  Leading attackers through attack graphs with deceptions , 2003, Comput. Secur..

[12]  Michael Kearns,et al.  Machine Learning for Market Microstructure and High Frequency Trading , 2013 .

[13]  Philippe Jehiel,et al.  A theory of deception , 2010 .

[14]  Pingzhong Tang,et al.  A Closed-Form Characterization of Buyer Signaling Schemes in Monopoly Pricing , 2018, AAMAS.

[15]  Xin He,et al.  Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models , 2019, 2019 IEEE International Conference on Embedded Software and Systems (ICESS).

[16]  Emir Kamenica,et al.  Bayesian Persuasion , 2009 .

[17]  Quanyan Zhu,et al.  Deception by Design: Evidence-Based Signaling Games for Network Defense , 2015, WEIS.

[18]  Haifeng Xu,et al.  Deceiving Cyber Adversaries: A Game Theoretic Approach , 2018, AAMAS.

[19]  Michael Kearns,et al.  Reinforcement learning for optimized trade execution , 2006, ICML.

[20]  David Easley,et al.  High-frequency trading : new realities for traders, markets and regulators , 2013 .

[21]  Mohammed H. Almeshekah,et al.  Cyber Security Deception , 2016, Cyber Deception.

[22]  Frank J. Stech,et al.  Integrating Cyber-D&D into Adversary Modeling for Active Cyber Defense , 2016, Cyber Deception.

[23]  Murat Kantarcioglu,et al.  Adversarial Machine Learning , 2018, Adversarial Machine Learning.