3 research outputs found
A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization
Data ecosystems are becoming larger and more complex due to online tracking,
wearable computing, and the Internet of Things. But privacy concerns are
threatening to erode the potential benefits of these systems. Recently, users
have developed obfuscation techniques that issue fake search engine queries,
undermine location tracking algorithms, or evade government surveillance.
Interestingly, these techniques raise two conflicts: one between each user and
the machine learning algorithms which track the users, and one between the
users themselves. In this paper, we use game theory to capture the first
conflict with a Stackelberg game and the second conflict with a mean field
game. We combine both into a dynamic and strategic bi-level framework which
quantifies accuracy using empirical risk minimization and privacy using
differential privacy. In equilibrium, we identify necessary and sufficient
conditions under which 1) each user is incentivized to obfuscate if other users
are obfuscating, 2) the tracking algorithm can avoid this by promising a level
of privacy protection, and 3) this promise is incentive-compatible for the
tracking algorithm.Comment: IEEE Global SIP Symposium on Control & Information Theoretic
Approaches to Privacy and Securit
Game-Theoretic Analysis of Cyber Deception: Evidence-Based Strategies and Dynamic Risk Mitigation
Deception is a technique to mislead human or computer systems by manipulating
beliefs and information. For the applications of cyber deception,
non-cooperative games become a natural choice of models to capture the
adversarial interactions between the players and quantitatively characterizes
the conflicting incentives and strategic responses. In this chapter, we provide
an overview of deception games in three different environments and extend the
baseline signaling game models to include evidence through side-channel
knowledge acquisition to capture the information asymmetry, dynamics, and
strategic behaviors of deception. We analyze the deception in binary
information space based on a signaling game framework with a detector that
gives off probabilistic evidence of the deception when the sender acts
deceptively. We then focus on a class of continuous one-dimensional information
space and take into account the cost of deception in the signaling game. We
finally explore the multi-stage incomplete-information Bayesian game model for
defensive deception for advanced persistent threats (APTs). We use the perfect
Bayesian Nash equilibrium (PBNE) as the solution concept for the deception
games and analyze the strategic equilibrium behaviors for both the deceivers
and the deceivees.Comment: arXiv admin note: text overlap with arXiv:1810.0075
Prospect Theoretic Analysis of Privacy-Preserving Mechanism
We study a problem of privacy-preserving mechanism design. A data collector
wants to obtain data from individuals to perform some computations. To relieve
the privacy threat to the contributors, the data collector adopts a
privacy-preserving mechanism by adding random noise to the computation result,
at the cost of reduced accuracy. Individuals decide whether to contribute data
when faced with the privacy issue. Due to the intrinsic uncertainty in privacy
protection, we model individuals' privacy-related decision using Prospect
Theory. Such a theory more accurately models individuals' behavior under
uncertainty than the traditional expected utility theory, whose prediction
always deviates from practical human behavior. We show that the data
collector's utility maximization problem involves a polynomial of high and
fractional order, the root of which is difficult to compute analytically. We
get around this issue by considering a large population approximation, and
obtain a closed-form solution that well approximates the precise solution. We
discover that the data collector who considers the more realistic Prospect
Theory based individual decision modeling would adopt a more conservative
privacy-preserving mechanism, compared with the case based on the expected
utility theory modeling. We also study the impact of Prospect Theory
parameters, and concludes that more loss-averse or risk-seeking individuals
will trigger a more conservative mechanism. When individuals have different
Prospect Theory parameters, simulations demonstrate that the privacy protection
first becomes stronger and then becomes weaker as the heterogeneity increases
from a low value to a high one.Comment: Accepted by IEEE/ACM Transactions on Networkin