Google's Trail of Crumbs
Matt Stoller published my essay on Google’s decision to abandon its Privacy Sandbox Initiative in his Big newsletter:
For more technical background on this, see Minjun’s paper: Evaluating Google’s Protected Audience Protocol in PETS 2024.
Congratulations, Dr. Suri!
Congratulations to Anshuman Suri for successfully defending his PhD thesis! Tianhao Wang, Dr. Anshuman Suri, Nando Fioretto, Cong Shen On Screen: David Evans, Giuseppe Ateniese Inference Privacy in Machine Learning Using machine learning models comes at the risk of leaking information about data used in their training and deployment. This leakage can expose sensitive information about properties of the underlying data distribution, data from participating users, or even individual records in the training data.Graduation 2024
Congratulations to our two PhD graduates!
Suya will be joining the University of Tennessee at Knoxville as an Assistant Professor.
Josie will be building a medical analytics research group at Dexcom.
Congratulations, Dr. Lamp!
Tianhao Wang (Committee Chair), Miaomiao Zhang, Lu Feng (Co-Advisor), Dr. Josie Lamp, David Evans On screen: Sula Mazimba, Rich Nguyen, Tingting Zhu Congratulations to Josephine Lamp for successfully defending her PhD thesis! Trustworthy Clinical Decision Support Systems for Medical Trajectories The explosion of medical sensors and wearable devices has resulted in the collection of large amounts of medical trajectories. Medical trajectories are time series that provide a nuanced look into patient conditions and their changes over time, allowing for a more fine-grained understanding of patient health.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): 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 Article
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.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.
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.Virginia Consumer Data Protection Act
Josephine Lamp presented on the new data privacy law that is pending in Virginia (it still needs a few steps including expected signing by governor, but likely to go into effect Jan 1, 2023): Slides (PDF)
This article provides a summary of the law: Virginia Passes Consumer Privacy Law; Other States May Follow, National Law Review, 17 February 2021.
The law itself is here: SB 1392: Consumer Data Protection Act
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: