Model-Targeted Poisoning Attacks with Provable Convergence
(Post by Sean Miller, using images adapted from Suya’s talk slides) Data Poisoning Attacks Machine learning models are often trained using data from untrusted sources, leaving them open to poisoning attacks where adversaries use their control over a small fraction of that training data to poison the model in a particular way. Most work on poisoning attacks is directly driven by an attacker’s objective, where the adversary chooses poisoning points that maximize some target objective.ICLR DPML 2021: Inference Risks for Machine Learning
I gave an invited talk at the Distributed and Private Machine Learning (DPML) workshop at ICLR 2021 on Inference Risks for Machine Learning.
The talk mostly covers work by Bargav Jayaraman on evaluating privacy in machine learning and connecting attribute inference and imputation, and recent work by Anshuman Suri on property inference.
How to Hide a Backdoor
The Register has an article on our recent work on Stealthy Backdoors as Compression Artifacts: Thomas Claburn, How to hide a backdoor in AI software — Neural networks can be aimed to misbehave when squeezed, The Register, 5 May 2021.
Codaspy 2021 Keynote: When Models Learn Too Much
Here are the slides for my talk at the 11th ACM Conference on Data and Application Security and Privacy: When Models Learn Too Much [PDF] The talk includes Bargav Jayaraman’s work (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) on evaluating privacy in machine learning, as well as more recent work by Anshuman Suri on property inference attacks, and Bargav on attribute inference and imputation: Merlin, Morgan, and the Importance of Thresholds and Priors Evaluating Differentially Private Machine Learning in Practice “When models learn too much.CrySP Talk: When Models Learn Too Much
I gave a talk 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.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.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:
- Merlin, Morgan, and the Importance of Thresholds and Priors
- Evaluating Differentially Private Machine Learning in Practice
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?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.Hybrid Batch Attacks at USENIX Security 2020
Here’s the video for Suya’s presentation on Hybrid Batch Attacks at USENIX Security 2020:
Download Video [mp4]