Our paper on the use of cryptographic-style games to model inference privacy is published in IEEE Symposium on Security and Privacy (Oakland):
Giovanni Cherubin, , Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, and Santiago Zanella-Béguelin. SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning. IEEE Symposium on Security and Privacy, 2023. [Arxiv]
Manipulating Transfer Learning for Property Inference Transfer learning is a popular method to train deep learning models efficiently. By reusing parameters from upstream pre-trained models, the downstream trainer can use fewer computing resources to train downstream models, compared to training models from scratch.
The figure below shows the typical process of transfer learning for vision tasks:
However, the nature of transfer learning can be exploited by a malicious upstream trainer, leading to severe risks to the downstream trainer.
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.
(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.
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.
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.
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.