Posts | Security Research Group

Oakland Test-of-Time Awards

23 May 2020

I chaired the committee to select Test-of-Time Awards for the IEEE Symposium on Security and Privacy symposia from 1995-2006, which were presented at the Opening Section of the 41^st IEEE Symposium on Security and Privacy.

NeurIPS 2019

16 December 2019 adversarial machine learning, Xiao Zhang, NeurIPS, Saeed Mahloujifar, Mohammad Mahmoody

Here's a video of Xiao Zhang's presentation at NeurIPS 2019:
https://slideslive.com/38921718/track-2-session-1 (starting at 26:50)

See this post for info on the paper.

Here are a few pictures from NeurIPS 2019 (by Sicheng Zhu and Mohammad Mahmoody):

USENIX Security 2020: Hybrid Batch Attacks

14 December 2019 adversarial machine learning, Fnu Suya, Yuan Tian, Jianfeng Chi, USENIX Security

New: Video Presentation Finding Black-box Adversarial Examples with Limited Queries Black-box attacks generate adversarial examples (AEs) against deep neural networks with only API access to the victim model. Existing black-box attacks can be grouped into two main categories: Transfer Attacks use white-box attacks on local models to find candidate adversarial examples that transfer to the target model. Optimization Attacks use queries to the target model and apply optimization techniques to search for adversarial examples.

NeurIPS 2019: Empirically Measuring Concentration

22 November 2019 adversarial machine learning, Xiao Zhang, NeurIPS, Saeed Mahloujifar, Mohammad Mahmoody

Xiao Zhang will present our work (with Saeed Mahloujifar and Mohamood Mahmoody) as a spotlight at NeurIPS 2019, Vancouver, 10 December 2019. Recent theoretical results, starting with Gilmer et al.‘s Adversarial Spheres (2018), show that if inputs are drawn from a concentrated metric probability space, then adversarial examples with small perturbation are inevitable.c The key insight from this line of research is that concentration of measure gives lower bound on adversarial risk for a large collection of classifiers (e.

White House Visit

8 November 2019 conferences, privacy, multi-party computation, White House

I had a chance to visit the White House for a Roundtable on Accelerating Responsible Sharing of Federal Data. The meeting was held under “Chatham House Rules”, so I won’t mention the other participants here. The meeting was held in the Roosevelt Room of the White House. We entered through the visitor’s side entrance. After a security gate (where you put your phone in a lockbox, so no pictures inside) with a TV blaring Fox News, there is a pleasant lobby for waiting, and then an entrance right into the Roosevelt Room.

Jobs for Humans, 2029-2059

30 October 2019 artificial intelligence, outreach, education

I was honored to particilate in a panel at an event on Adult Education in the Age of Artificial Intelligence that was run by The Great Courses as a fundraiser for the Academy of Hope, an adult public charter school in Washington, D.C. I spoke first, following a few introductory talks, and was followed by Nicole Smith and Ellen Scully-Russ, and a keynote from Dexter Manley, Super Bowl winner with the Washington Redskins.

Research Symposium Posters

8 October 2019 research, adversarial machine learning, privacy, Mainuddin Jonas, Xiao Zhang, Bargav Jayaraman, Hannah Chen, Fnu Suya, Anshuman Suri

Five students from our group presented posters at the department’s Fall Research Symposium:

Anshuman Suri's Overview Talk

Bargav Jayaraman, Evaluating Differentially Private Machine Learning In Practice [Poster]
[Paper (USENIX Security 2019)]

Hannah Chen [Poster]

Xiao Zhang [Poster ]
[Paper (NeurIPS 2019)]

Mainudding Jonas [Poster]

Fnu Suya [Poster ]
[Paper (USENIX Security 2020)]

Cantor's (No Longer) Lost Proof

26 September 2019 uncountability, teaching, history

In preparing to cover Cantor’s proof of different infinite set cardinalities (one of my all-time favorite topics!) in our theory of computation course, I found various conflicting accounts of what Cantor originally proved. So, I figured it would be easy to search the web to find the original proof. Shockingly, at least as far as I could find1, it didn’t exist on the web! The closest I could find was in Google Books the 1892 volume of the Jähresbericht Deutsche Mathematiker-Vereinigung (which many of the references pointed to), but in fact not the first value of that journal which contains the actual proof.

FOSAD Trustworthy Machine Learning Mini-Course

28 August 2019 privacy, adversarial machine learning

I taught a mini-course on Trustworthy Machine Learning at the 19th International School on Foundations of Security Analysis and Design in Bertinoro, Italy. Slides from my three (two-hour) lectures are posted below, along with some links to relevant papers and resources. Class 1: Introduction/Attacks The PDF malware evasion attack is described in this paper: Weilin Xu, Yanjun Qi, and David Evans. Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers.

Evaluating Differentially Private Machine Learning in Practice

27 August 2019 privacy, privacy-preserving machine learning, Bargav Jayaraman, differential privacy

(Cross-post by Bargav Jayaraman) With the recent advances in composition of differential private mechanisms, the research community has been able to achieve meaningful deep learning with privacy budgets in single digits. Rènyi differential privacy (RDP) is one mechanism that provides tighter composition which is widely used because of its implementation in TensorFlow Privacy (recently, Gaussian differential privacy (GDP) has shown a tighter analysis for low privacy budgets, but it was not yet available when we did this work).

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