Michael Reiter

About

Michael Reiter is a James B. Duke Distinguished Professor of Computer Science and Electrical & Computer Engineering at Duke University. His research interests include all areas of computer and communications security, fault-tolerant distributed computing, and applied cryptography. He has previously held positions such as Director of Secure Systems Research at Bell Labs, Professor of Electrical & Computer Engineering and Computer Science at Carnegie Mellon University where he was the founding Technical Director of CyLab, and Distinguished Professor of Computer Science at the University of North Carolina at Chapel Hill. His academic background includes a Ph.D. from Cornell University obtained in 1993. Reiter has received numerous awards and honors, including the ACM Test of Time Award, the Lasting Research Award, and fellowships from IEEE and ACM. His teaching includes courses on cryptography, computer security, and research independent studies, reflecting his extensive involvement in advancing security research and education.

Research topics

• Computer Science
• Computer Security
• Artificial Intelligence
• Machine Learning
• Computer network
• Algorithm
• Geometry
• Mathematics
• Operating system
• Computer vision

Selected publications

• Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes

  2021 · 60 citations

  Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we propose an attack that interweaves binary-diversification techniques and optimization frameworks to mislead such DNNs while preserving the functionality of binaries. Unlike prior attacks, ours manipulates instructions that are a functional part of the bi…

  DOI

• ENGRAFT

  Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security · 2022 · 20 citations

  This paper presents the first critical analysis of building highly secure, performant, and confidential Byzantine fault-tolerant (BFT) consensus by integrating off-the-shelf crash fault-tolerant (CFT) protocols with trusted execution environments (TEEs). TEEs, like Intel SGX, are CPU extensions that offer applications a secure execution environment with strong integrity and confidentiality guarantees, by leveraging techniques like hardware-assisted isolation, memory encryption, and remote attest…

  DOI

• Delphi: Efficient Asynchronous Approximate Agreement for Distributed Oracles

  2024-06-24 · 7 citations

  articleSenior author

  Agreement protocols are crucial in various emerging applications, spanning from distributed (blockchains) oracles to fault-tolerant cyber-physical systems. In scenarios where sensor/oracle nodes measure a common source, maintaining output within the convex range of correct inputs, known as convex validity, is imperative. Present asynchronous convex agreement protocols employ either randomization, incurring substantial computation overhead, or approximate agreement techniques, leading to high <te…

  PublisherDOI

• Random Beacons in Monte Carlo: Efficient Asynchronous Random Beacon <i>without</i> Threshold Cryptography

  2024-12-02 · 6 citations

  articleOpen accessSenior author

  Regular access to unpredictable and bias-resistant randomness is important for applications such as blockchains, voting, and secure distributed computing. Distributed random beacon protocols address this need by distributing trust across multiple nodes, with the majority of them assumed to be honest. Numerous applications across the blockchain space have led to the proposal of several distributed random beacon protocols, with some already implemented. However, many current random beacon systems…

  PublisherOA PDFDOI

• A General Framework for Data-Use Auditing of ML Models

  2024-12-02 · 5 citations

  articleOpen accessSenior author

  Auditing the use of data in training machine-learning (ML) models is an increasingly pressing challenge, as myriad ML practitioners routinely leverage the effort of content creators to train models without their permission. In this paper, we propose a general method to audit an ML model for the use of a data-owner's data in training, without prior knowledge of the ML task for which the data might be used. Our method leverages any existing black-box membership inference method, together with a se…

  PublisherOA PDFDOI

Recent grants

• AitF: FULL: Collaborative Research: Practical Foundations for Software-Defined Network Optimization

  NSF · $173k · 2015–2019

• SaTC: CORE: Medium: Collaborative: Using Machine Learning to Build More Resilient and Transparent Computer Systems

  NSF · $333k · 2018–2021

• FIA: Collaborative Research: MobilityFirst: A Robust and Trustworthy Mobility-Centric Architecture for the Future Internet

  NSF · $525k · 2010–2015

Frequent coauthors

• Lujo Bauer

  Carnegie Mellon University

  50 shared

• Dahlia Malkhi

  University of California, Santa Barbara

  43 shared

• Vyas Sekar

  Carnegie Mellon University

  33 shared

• Gregory R. Ganger

  23 shared

• Yinqian Zhang

  20 shared

• Helmut Hofer

  20 shared

• Klaus Weyerstraß

  Institut für Höhere Studien - Institute for Advanced Studies (IHS)

  20 shared

• Fabian Monrose

  Georgia Institute of Technology

  20 shared

Labs

• Michael Reiter LabPI

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1990

• M.S., Computer Science

  University of California, Berkeley

  1986

• B.S., Electrical Engineering and Computer Science

  University of California, Berkeley

  1984

Awards & honors

• Test of Time Award. Intel Hardware Security Academic Awards…
• Lasting Research Award. ACM Conference on Data and Applicati…
• Test of Time Award. ACM Conference on Computer and Communica…
• Test of Time Award. ACM Conference on Computer and Communica…
• Fellow. IEEE (2016)

Similar researchers at Duke University

• David R. Smithh-141
• Christopher Monroeh-140
• Jian Peih-96