But when you add something new to an ML pipeline, there's new ways for adversaries to wreak havoc. We introduce a new type of data poisoning attack exploiting the dynamic nature of unlearn requests: a "camouflage" attack, which lies dormant until triggered by the adversary. 3/n
@thegautamkamath
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Machine Unlearning Vulnerability Enables Poisoning Attacks
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New paper led by Jimmy Di & Jack Douglas, co-advised with @AcharyaJayadev @ayush_sekhari
: "Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks" The adaptive nature of machine unlearning opens a new attack vector for adversaries. https://
arxiv.org/abs/2212.10717 1/n -
Differential Privacy Tutorial at SaTML Conference
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Looking forward to giving a tutorial on differential privacy at the inaugural SaTML! https://
x.com/NicolasPaperno
/NicolasPapernot/status/1613579758792572928
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Differential Privacy Results Beyond Pure DP Guarantees
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Everything above is for pure (ε, 0)-DP. We also have results for (ε, δ)-DP. Also, the conversion from robustness to privacy is not always optimal, we have an example for sparse mean estimation. 11/n
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Robust Gaussian Estimation: Novel Techniques in Statistical Learning
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Finally, see simultaneous work of Alabi, @praveshkkothari
, Tankala, Venkat, Zhang, which also studies private & robust Gaussian estimation from a robust stats perspective. Totally different techniques! https://
arxiv.org/abs/2212.08018 Again, our paper link: https://
arxiv.org/abs/2212.05015 12/12 -
Computational Challenges in Statistical Estimator Implementation
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The astute reader will notice I never mentioned computation. Indeed, as stated, it's not clear how to compute this estimator efficiently (or at all!). That's where most of the technical work comes in. I'll leave you with this theorem, check out the rest of the paper. 10/n
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Private Robust Gaussian Estimation Framework Near-Optimal Sample Complexity
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So what can we do with this framework? The most interesting application is estimating Gaussians privately AND robustly: the resulting algorithm nails it, with a near-optimal sample complexity. Other potential applications include mean estimation, regression, etc. 9/n
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Robust Estimators and Exponential Mechanism for Parameter Scoring
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The notation is a dense, but the algorithm is simple. The score of a parameter vector θ (wrt a dataset) is the minimum number of datapoints that must be changed to make the robust estimator output (approximately) θ. Feed this score into the exponential mechanism. 8/n
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Robust Algorithms Enable Private Algorithm Design
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In our work, the main conceptual result shows the other direction: a robust algorithm can be used to design a private algorithm. 7/n
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Differential Privacy Frameworks for Robust Statistical Estimators
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Most have been instance-specific, e.g., here's a particular robust estimator, let's privatize it. Some have proposed broader frameworks, e.g., https://
arxiv.org/abs/2111.06578 by Liu, Kong and @sewoong79
, or https://
arxiv.org/abs/2112.03548 by @praveshkkothari @pasin30055 @ameya_pa 5/n