MICO Challenge in Membership Inference
Anshuman Suri wrote up an interesting post on his experience with the MICO Challenge, a membership inference competition that was part of SaTML. Anshuman placed second in the competition (on the CIFAR data set), where the metric is highest true positive rate at a 0.1 false positive rate over a set of models (some trained using differential privacy and some without).
Anshuman’s post describes the methods he used and his experience in the competition: My submission to the MICO Challenge.
Voice of America interview on ChatGPT
I was interviewed for a Voice of America story (in Russian) on the impact of chatGPT and similar tools.
Full story: https://youtu.be/dFuunAFX9y4
Uh-oh, there's a new way to poison code models
Jack Clark’s Import AI, 16 Jan 2023 includes a nice description of our work on TrojanPuzzle:
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Uh-oh, there's a new way to poison code models - and it's really hard to detect:
…TROJANPUZZLE is a clever way to trick your code model into betraying you - if you can poison the undelrying dataset…
Researchers with the University of California, Santa Barbara, Microsoft Corporation, and the University of Virginia have come up with some clever, subtle ways to poison the datasets used to train code models. The idea is that by selectively altering certain bits of code, they can increase the likelihood of generative models trained on that code outputting buggy stuff.
Trojan Puzzle attack trains AI assistants into suggesting malicious code
Bleeping Computer has a story on our work (in collaboration with Microsoft Research) on poisoning code suggestion models:
Trojan Puzzle attack trains AI assistants into suggesting malicious code
By Bill Toulas
Researchers at the universities of California, Virginia, and Microsoft have devised a new poisoning attack that could trick AI-based coding assistants into suggesting dangerous code.
Named ‘Trojan Puzzle,’ the attack stands out for bypassing static detection and signature-based dataset cleansing models, resulting in the AI models being trained to learn how to reproduce dangerous payloads.
Dissecting Distribution Inference
(Cross-post by Anshuman Suri)
Distribution inference attacks aims to infer statistical properties of data used to train machine learning models. These attacks are sometimes surprisingly potent, as we demonstrated in previous work.
KL Divergence Attack
Most attacks against distribution inference involve training a meta-classifier, either using model parameters in white-box settings (Ganju et al., Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations, CCS 2018), or using model predictions in black-box scenarios (Zhang et al., Leakage of Dataset Properties in Multi-Party Machine Learning, USENIX 2021). While other black-box were proposed in our prior work, they are not as accurate as meta-classifier-based methods, and require training shadow models nonetheless (Suri and Evans, Formalizing and Estimating Distribution Inference Risks, PETS 2022).
Cray Distinguished Speaker: On Leaky Models and Unintended Inferences
Here’s the slides from my Cray Distinguished Speaker talk on On Leaky Models and Unintended Inferences: [PDF]
The chatGPT limerick version of my talk abstract is much better than mine:
A machine learning model, oh so grand
With data sets that it held in its hand
It performed quite well
But secrets to tell
And an adversary’s tricks it could not withstand.
Thanks to Stephen McCamant and Kangjie Lu for hosting my visit, and everyone at University of Minnesota. Also great to catch up with UVA BSCS alumn, Stephen J. Guy.
Attribute Inference attacks are really Imputation
Post by Bargav Jayaraman
Attribute inference attacks have been shown by prior works to pose privacy threat against ML models. However, these works assume the knowledge of the training distribution and we show that in such cases these attacks do no better than a data imputataion attack that does not have access to the model. We explore the attribute inference risks in the cases where the adversary has limited or no prior knowledge of the training distribution and show that our white-box attribute inference attack (that uses neuron activations to infer the unknown sensitive attribute) surpasses imputation in these data constrained cases. This attack uses the training distribution information leaked by the model, and thus poses privacy risk when the distribution is private.
Congratulations, Dr. Jayaraman!
Congratulations to Bargav Jayaraman for successfully defending his PhD thesis!
Bargav will join the Meta AI Lab in Menlo Park, CA as a post-doctoral researcher.
Machine learning models have been shown to leak sensitive information about their training data. An adversary having access to the model can infer different types of sensitive information, such as learning if a particular individual’s data is in the training set, extracting sensitive patterns like passwords in the training set, or predicting missing sensitive attribute values for partially known training records. This dissertation quantifies this privacy leakage. We explore inference attacks against machine learning models including membership inference, pattern extraction, and attribute inference. While our attacks give an empirical lower bound on the privacy leakage, we also provide a theoretical upper bound on the privacy leakage metrics. Our experiments across various real-world data sets show that the membership inference attacks can infer a subset of candidate training records with high attack precision, even in challenging cases where the adversary’s candidate set is mostly non-training records. In our pattern extraction experiments, we show that an adversary is able to recover email ids, passwords and login credentials from large transformer-based language models. Our attribute inference adversary is able to use underlying training distribution information inferred from the model to confidently identify candidate records with sensitive attribute values. We further evaluate the privacy risk implication to individuals contributing their data for model training. Our findings suggest that different subsets of individuals are vulnerable to different membership inference attacks, and that some individuals are repeatedly identified across multiple runs of an attack. For attribute inference, we find that a subset of candidate records with a sensitive attribute value are correctly predicted by our white-box attribute inference attacks but would be misclassified by an imputation attack that does not have access to the target model. We explore different defense strategies to mitigate the inference risks, including approaches that avoid model overfitting such as early stopping and differential privacy, and approaches that remove sensitive data from the training. We find that differential privacy mechanisms can thwart membership inference and pattern extraction attacks, but even differential privacy fails to mitigate the attribute inference risks since the attribute inference attack relies on the distribution information leaked by the model whereas differential privacy provides no protection against leakage of distribution statistics.
Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models
Post by Hannah Chen.
Our work on balanced adversarial training looks at how to train models that are robust to two different types of adversarial examples:
Hannah Chen, Yangfeng Ji, David Evans. Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models. In The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, 7-11 December 2022. [ArXiv]
Adversarial Examples
At the broadest level, an adversarial example is an input crafted intentionally to confuse a model. However, most work focus on the defintion as an input constructed by applying a small perturbation that preserves the ground truth label but changes model’s output (Goodfellow et al., 2015). We refer it as a fickle adversarial example. On the other hand, attackers can target an opposite objective where the inputs are made with minimal changes that change the ground truth labels but retain model’s predictions (Jacobsen et al., 2018). We refer these malicious inputs as obstinate adversarial examples.
Best Submission Award at VISxAI 2022
Poisoning Attacks and Subpopulation Susceptibility by Evan Rose, Fnu Suya, and David Evans won the Best Submission Award at the 5th Workshop on Visualization for AI Explainability.
Undergraduate student Evan Rose led the work and presented it at VISxAI in Oklahoma City, 17 October 2022.
Congratulations to #VISxAI's Best Submission Awards:
— VISxAI (@VISxAI) October 17, 2022
🏆 K-Means Clustering: An Explorable Explainer by @yizhe_ang https://t.co/BULW33WPzo
🏆 Poisoning Attacks and Subpopulation Susceptibility by Evan Rose, @suyafnu, and @UdacityDave https://t.co/Z12D3PvfXu#ieeevis
Next up is best submission award 🏅 winner, "Poisoning Attacks and Subpopulation Susceptibility" by Evan Rose, @suyafnu, and @UdacityDave.
Tune in to learn why some data subpopulations are more vulnerable to attacks than others!https://t.co/Z12D3PvfXu#ieeevis #VISxAI pic.twitter.com/Gm2JBpWQSP