Microsoft Research Summit: Surprising (and unsurprising) Inference Risks in Machine Learning

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):

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. Revisiting Membership Inference Under Realistic Assumptions (PETS 2021).
  [Blog] [Code: https://github.com/bargavj/EvaluatingDPML]

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  UVA News Article

  28 September 2021 adversarial machine learning, privacy, Bargav Jayaraman, Xiao Zhang, Jack Prescott, Anshuman Suri, Katherine Knipmeyer, inference privacy, privacy, privacy-preserving machine learning

  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.

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  ICLR DPML 2021: Inference Risks for Machine Learning

  7 May 2021 adversarial machine learning, privacy, Bargav Jayaraman, Anshuman Suri, Katherine Knipmeyer, inference privacy, privacy, privacy-preserving 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.

  Codaspy 2021 Keynote: When Models Learn Too Much

  26 April 2021 adversarial machine learning, privacy, Bargav Jayaraman, Anshuman Suri, Katherine Knipmeyer, inference privacy, privacy, privacy-preserving machine learning

  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. “ Dr. David Evans @UdacityDave of University of Virginia gave a keynote talk on different inference risks for machine learning models this morning at #codaspy21 pic.twitter.com/KVgFoUA6sa

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  Merlin, Morgan, and the Importance of Thresholds and Priors

  2 October 2020 privacy, privacy-preserving machine learning, Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu

  Post by Katherine Knipmeyer

  Machine learning poses a substantial risk that adversaries will be able to discover information that the model does not intend to reveal. One set of methods by which consumers can learn this sensitive information, known broadly as membership inference attacks, predicts whether or not a query record belongs to the training set. A basic membership inference attack involves an attacker with a given record and black-box access to a model who tries to determine whether said record was a member of the model’s training set.

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