David Evans

About

David Evans is a professor at the University of Virginia Department of Computer Science. His teaching portfolio spans a wide range of computer science topics, including introductory courses such as "Introduction to Information Technology" and "Introduction to Computing: Explorations in Language, Logic, and Machines," as well as advanced subjects like "Theory of Computation," "Operating Systems," "Cryptology," and "Artificial Intelligence and Machine Learning." He has developed and taught numerous courses both at the undergraduate and graduate levels, including interdisciplinary courses co-taught with faculty from other departments such as Economics and Law. Evans has also contributed to online education through Udacity, offering popular courses like "Introduction to Computer Science" and "Applied Cryptography," which have attracted hundreds of thousands of students worldwide. His outreach efforts include cryptography lessons for high school students and specialized seminars for professionals and lifelong learners. Throughout his career, Evans has focused on integrating foundational computer science concepts with practical applications, emphasizing security, privacy, ethics, and the societal impacts of computing technologies.

Research topics

• Machine Learning
• Computer Security
• Artificial Intelligence
• Computer Science
• Physics
• Mathematics
• Psychology
• Combinatorics
• Engineering

Selected publications

• Revisiting Membership Inference Under Realistic Assumptions

  Proceedings on Privacy Enhancing Technologies · 2021 · 76 citations

  Senior authorCorresponding

  We study membership inference in settings where assumptions commonly used in previous research are relaxed. First, we consider cases where only a small fraction of the candidate pool targeted by the adversary are members and develop a PPV-based metric suitable for this setting. This skewed prior setting is more realistic than the balanced prior setting typically considered. Second, we consider adversaries that select inference thresholds according to their attack goals, such as identifying as ma…

  DOI

• Are Attribute Inference Attacks Just Imputation?

  Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security · 2022-11-07 · 42 citations

  articleOpen accessSenior author

  Models can expose sensitive information about their training data. In an attribute inference attack, an adversary has partial knowledge of some training records and access to a model trained on those records, and infers the unknown values of a sensitive feature of those records. We study a fine-grained variant of attribute inference we call sensitive value inference, where the adversary's goal is to identify with high confidence some records from a candidate set where the unknown attribute has a…

  PublisherOA PDFDOI

• Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment

  Harvard Data Science Review · 2024-01-16 · 35 citations

  articleOpen access

  In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP’s deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from “Differential Privacy (DP): Challenges Towards the Next Frontier,” a workshop held in July 2022 with experts from industry, academia, and the public sector seeking answers to broad questi…

  PublisherOA PDFDOI

• Formalizing and Estimating Distribution Inference Risks

  Proceedings on Privacy Enhancing Technologies · 2022-08-31 · 31 citations

  articleOpen accessSenior author

  Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data. Distribution inference attacks can pose serious risks when models are trained on private data, but are difficult to distinguish from the intrinsic purpose of statistical machine learning—namely, to produce models that capture statistical properties about a distribution. Motivated by Yeom et al.’s membership inference framework, we propose a…

  PublisherOA PDFDOI

• Do Membership Inference Attacks Work on Large Language Models?

  arXiv (Cornell University) · 2024-02-12 · 10 citations

  preprintOpen access

  Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pre-training data of large language models (LLMs). We perform a large-scale evaluation of MIAs over a suite of language models (LMs) trained on the Pile, ranging from 160M to 12B parameters. We find that MIAs barely outperform random guessing for most…

  PublisherOA PDFDOI

Recent grants

• TWC: Small: Automated Security Testing for Applications Integrating Third-Party Services

  NSF · $500k · 2014–2019

• Automatic Inference and Effective Application of Temporal Specifications

  NSF · $330k · 2006–2010

• CT-ER: Automatic Identification and Protection of Security Critical Data

  NSF · $200k · 2006–2009

Frequent coauthors

• Yasuyuki Kawahigashi

  28 shared

• Mathew Pugh

  22 shared

• Ola Bratteli

  17 shared

• Akitaka Kishimoto

  13 shared

• Anshuman Suri

  University of Virginia

  10 shared

• George A. Elliott

  University of Toronto

  10 shared

• David Eyers

  10 shared

• Jean Bacon

  Grinnell College

  10 shared

Labs

• Security Research GroupPI

Awards & honors

• ACM Conference on Computer and Communications Security PC Co…
• Distinguished Research Award 2014
• IEEE Technical Committee on Security and Privacy Award for O…
• State Council of Higher Education for Virginia Outstanding F…
• Defense Science Study Group Fellow 2008–9

Similar researchers at University of Virginia

• Kevin Skadronh-63
• Qizhe Caih-57
• Jundong Lih-56