When Relaxations Go Bad: "Differentially-Private" Machine Learning

We have posted a paper by Bargav Jayaraman and myself on When Relaxations Go Bad: “Differentially-Private” Machine Learning (code available at https://github.com/bargavj/EvaluatingDPML). Differential privacy is becoming a standard notion for performing privacy-preserving machine learning over sensitive data. It provides formal guarantees, in terms of the privacy budget, ε, on how much information about individual training records is leaked by the model. While the privacy budget is directly correlated to the privacy leakage, the calibration of the privacy budget is not well understood.

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Deep Fools

New Electronics has an article that includes my Deep Learning and Security Workshop talk: Deep fools, 21 January 2019.

A better version of the image Mainuddin Jonas produced that they use (which they screenshot from the talk video) is below:

Markets, Mechanisms, Machines

My course for Spring 2019 is Markets, Mechanisms, Machines, cross-listed as cs4501/econ4559 and co-taught with Denis Nekipelov. The course will explore interesting connections between economics and computer science. My qualifications for being listed as instructor for a 4000-level Economics course are limited to taking an introductory microeconomics course my first year as an undergraduate. Its good to finally get a chance to redeem myself for giving up on Economics 28 years ago!

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ICLR 2019: Cost-Sensitive Robustness against Adversarial Examples

Xiao Zhang and my paper on Cost-Sensitive Robustness against Adversarial Examples has been accepted to ICLR 2019. Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications. We advocate for cost-sensitive robustness as the criteria for measuring the classifier’s performance for specific tasks.

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A Pragmatic Introduction to Secure Multi-Party Computation

A Pragmatic Introduction to Secure Multi-Party Computation, co-authored with Vladimir Kolesnikov and Mike Rosulek, is now published by Now Publishers in their Foundations and Trends in Privacy and Security series. You can download the book for free (we retain the copyright and are allowed to post an open version) from securecomputation.org, or buy an PDF version from the published for $260 (there is also a printed $99 version). Secure multi-party computation (MPC) has evolved from a theoretical curiosity in the 1980s to a tool for building real systems today.

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NeurIPS 2018: Distributed Learning without Distress

Bargav Jayaraman presented our work on privacy-preserving machine learning at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) in Montreal. Distributed learning (sometimes known as federated learning) allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. Our approach combines differential privacy with secure multi-party computation to both protect the data during training and produce a model that provides privacy against inference attacks.

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Can Machine Learning Ever Be Trustworthy?

I gave the Booz Allen Hamilton Distinguished Colloquium at the University of Maryland on Can Machine Learning Ever Be Trustworthy?. Video · SpeakerDeck Abstract Machine learning has produced extraordinary results over the past few years, and machine learning systems are rapidly being deployed for critical tasks, even in adversarial environments. This talk will survey some of the reasons building trustworthy machine learning systems is inherently impossible, and dive into some recent research on adversarial examples.

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Center for Trustworthy Machine Learning

The National Science Foundation announced the Center for Trustworthy Machine Learning today, a new five-year SaTC Frontier Center “to develop a rigorous understanding of the security risks of the use of machine learning and to devise the tools, metrics and methods to manage and mitigate security vulnerabilities.” The Center is lead by Patrick McDaniel at Penn State University, and in addition to our group, includes Dan Boneh and Percy Liang (Stanford University), Kamalika Chaudhuri (University of California San Diego), Somesh Jha (University of Wisconsin) and Dawn Song (University of California Berkeley).

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Artificial intelligence: the new ghost in the machine

Engineering and Technology Magazine (a publication of the British Institution of Engineering and Technology has an article that highlights adversarial machine learning research: Artificial intelligence: the new ghost in the machine, 10 October 2018, by Chris Edwards. Although researchers such as David Evans of the University of Virginia see a full explanation being a little way off in the future, the massive number of parameters encoded by DNNs and the avoidance of overtraining due to SGD may have an answer to why the networks can hallucinate images and, as a result, see things that are not there and ignore those that are.

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Violations of Children’s Privacy Laws

The New York Times has an article, How Game Apps That Captivate Kids Have Been Collecting Their Data about a lawsuit the state of New Mexico is bringing against app markets (including Google) that allow apps presented as being for children in the Play store to violate COPPA rules and mislead users into tracking children. The lawsuit stems from a study led by Serge Egleman’s group at UC Berkeley that analyzed COPPA violations in children’s apps.

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