(Blog post written by Xiao Zhang)
Motivated by the empirical hardness of developing robust classifiers against adversarial perturbations, researchers began asking the question “Does there even exist a robust classifier?”. This is formulated as the intrinsic robustness problem (Mahloujifar et al., 2019), where the goal is to characterize the maximum adversarial robustness possible for a given robust classification problem. Building upon the connection between adversarial robustness and classifier’s error region, it has been shown that if we restrict the search to the set of imperfect classifiers, the intrinsic robustness problem can be reduced to the concentration of measure problem.
Here are the slides for my talk at the Practical and Theoretical Privacy of Machine Learning Training Pipelines Workshop at the Microsoft Research Summit (21 October 2021):
Surprising (and Unsurprising) Inference Risks in Machine Learning [PDF] The work by Bargav Jayaraman (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) that I talked about on improving membership inference attacks is described in more details here:
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans.
UVA News has an article by Audra Book on our research on security and privacy of machine learning (with some very nice quotes from several students in the group, and me saying something positive about the NSA!): Computer science professor David Evans and his team conduct experiments to understand security and privacy risks associated with machine learning, 8 September 2021.
David Evans, professor of computer science in the University of Virginia School of Engineering and Applied Science, is leading research to understand how machine learning models can be compromised.
(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.
(Cross-post by Anshuman Suri)
Inference attacks seek to infer sensitive information about the training process of a revealed machine-learned model, most often about the training data.
Standard inference attacks (which we call “dataset inference attacks”) aim to learn something about a particular record that may have been in that training data. For example, in a membership inference attack (Reza Shokri et al., Membership Inference Attacks Against Machine Learning Models, IEEE S&P 2017), the adversary aims to infer whether or not a particular record was included in the training data.
A Chinese translation of our A Pragmatic Introduction to Secure
Multi-Party Computation book (by David
Evans, Vladimir Kolesnikov, and Mike Rosulek) is now available!
Thanks to Weiran Liu and Sengchao Ding for all the work they
did on the translation.
To order from JD.com: https://item.jd.com/13302742.html
(The English version of the book is still available for free download, from https://securecomputation.org.)
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.
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.
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.
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.