Summary of Towards a Game-theoretic Understanding Of Explanation-based Membership Inference Attacks, by Kavita Kumari et al.
Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks
by Kavita Kumari, Murtuza Jadliwala, Sumit Kumar Jha, Anindya Maiti
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to explaining and improving the transparency of machine learning models is presented, but it also has the potential to be exploited for privacy threats like membership inference attacks (MIA). Existing works only analyzed MIA in a single scenario, neglecting factors that impact an adversary’s capabilities. This study delves into explanation-based threshold attacks, where an adversary leverages explanation variance through iterative interactions with the system. A continuous-time stochastic signaling game framework is employed to model these interactions, allowing for optimal threshold computation and membership determination. The paper proves the existence of an optimal threshold and characterizes conditions for a unique Markov perfect equilibrium. Simulation results assess factors impacting MIA capabilities in repeated interaction settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be transparent or black-box, but that’s not all. Some people want to know how they make decisions, which is called explanation. But bad guys might use this information to steal our secrets! Right now, we don’t fully understand how these attacks work when the model and its explanations interact many times. This study tries to change that by creating a game where the bad guy tries to figure out if something belongs or not based on the variance of explanations. They want to know what makes this possible and what stops it from happening. |
Keywords
» Artificial intelligence » Inference » Machine learning
