Summary of Some Targets Are Harder to Identify Than Others: Quantifying the Target-dependent Membership Leakage, by Achraf Azize et al.
Some Targets Are Harder to Identify than Others: Quantifying the Target-dependent Membership Leakage
by Achraf Azize, Debabrota Basu
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 This paper sheds light on the target-dependent hardness of membership attacks by analyzing the optimal attacks in a Membership Inference (MI) game, where an attacker tries to infer whether a target point was included or not in the input of an algorithm. The study characterizes the optimal advantage and trade-off functions of attacks against the empirical mean in terms of the Mahalanobis distance between the target point and the data-generating distribution. Additionally, the paper investigates the impacts of two privacy defenses, Gaussian noise addition and sub-sampling, as well as target misspecification on optimal attacks. As a byproduct, the authors provide a new covariance attack that generalizes and improves the scalar product attack. Furthermore, they propose an optimal canary-choosing strategy for auditing privacy in the white-box federated learning setting. Experimental results validate the Mahalanobis score’s ability to explain the hardness of fixed-target MI games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how hard it is to figure out whether a piece of data was used by an algorithm or not. The authors do this by looking at what they call “membership attacks” and how well different methods can guess if some target data was included in the input. They find that some data points are easier to identify than others, and that adding noise to the data or reducing the amount of data makes it harder for attackers to figure out which data points were used. The authors also come up with new ways to defend against these attacks and suggest a method for checking how well an algorithm is protecting its data. |
Keywords
* Artificial intelligence * Federated learning * Inference
