CrySP Talk: When Models Learn Too Much

I gave a talko on When Models Learn Too Much at the University of Waterloo (virtually) in the CrySP Speaker Series on Privacy (29 March 2021): Abstract Statistical machine learning uses training data to produce models that capture patterns in that data. When models are trained on private data, such as medical records or personal emails, there is a risk that those models not only learn the hoped-for patterns, but will also learn and expose sensitive information about their training data.

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Improved Estimation of Concentration (ICLR 2021)

Our paper on Improved Estimation of Concentration Under ℓp-Norm Distance Metrics Using Half Spaces (Jack Prescott, Xiao Zhang, and David Evans) will be presented at ICLR 2021. Abstract: Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets.

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Microsoft Security Data Science Colloquium: Inference Privacy in Theory and Practice

Here are the slides for my talk at the Microsoft Security Data Science Colloquium:
When Models Learn Too Much: Inference Privacy in Theory and Practice [PDF]

The talk is mostly about Bargav Jayaraman’s work (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) on evaluating privacy:

Adversarially Robust Representations

Post by Sicheng Zhu With the rapid development of deep learning and the explosive growth of unlabeled data, representation learning is becoming increasingly important. It has made impressive applications such as pre-trained language models (e.g., BERT and GPT-3). Popular as it is, representation learning raises concerns about the robustness of learned representations under adversarial settings. For example, how can we compare the robustness to different representations, and how can we build representations that enable robust downstream classifiers?

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Intrinsic Robustness using Conditional GANs

The video of Xiao’s presentation for AISTATS 2020 is now available: Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability space. It remains unclear, however, whether these results apply to natural image distributions. In this work, we assume the underlying data distribution is captured by some conditional generative model, and prove intrinsic robustness bounds for a general class of classifiers, which solves an open problem in Fawzi et al.

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Hybrid Batch Attacks at USENIX Security 2020

Here’s the video for Fnu Suya’s presentation on Hybrid Batch Attacks at USENIX Security 2020:


Download Video [mp4]

Blog Post
Paper: [PDF] [arXiv]

NeurIPS 2019

Here's a video of Xiao Zhang's presentation at NeurIPS 2019:
https://slideslive.com/38921718/track-2-session-1 (starting at 26:50)

See this post for info on the paper.

Here are a few pictures from NeurIPS 2019 (by Sicheng Zhu and Mohammad Mahmoody):






USENIX Security 2020: Hybrid Batch Attacks

New: Video Presentation Finding Black-box Adversarial Examples with Limited Queries Black-box attacks generate adversarial examples (AEs) against deep neural networks with only API access to the victim model. Existing black-box attacks can be grouped into two main categories: Transfer Attacks use white-box attacks on local models to find candidate adversarial examples that transfer to the target model. Optimization Attacks use queries to the target model and apply optimization techniques to search for adversarial examples.

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NeurIPS 2019: Empirically Measuring Concentration

Xiao Zhang will present our work (with Saeed Mahloujifar and Mohamood Mahmoody) as a spotlight at NeurIPS 2019, Vancouver, 10 December 2019. Recent theoretical results, starting with Gilmer et al.‘s Adversarial Spheres (2018), show that if inputs are drawn from a concentrated metric probability space, then adversarial examples with small perturbation are inevitable.c The key insight from this line of research is that concentration of measure gives lower bound on adversarial risk for a large collection of classifiers (e.

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Research Symposium Posters

Five students from our group presented posters at the department’s Fall Research Symposium:


Anshuman Suri's Overview Talk

Bargav Jayaraman, Evaluating Differentially Private Machine Learning In Practice [Poster]
[Paper (USENIX Security 2019)]




Hannah Chen [Poster]




Xiao Zhang [Poster]
[
Paper (NeurIPS 2019)]




Mainudding Jonas [Poster]




Fnu Suya [Poster]
[
Paper (USENIX Security 2020)]
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