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Michael Reiter

James B. Duke Distinguished Professor of Computer Science
Computer Science
308 Research Drive D310, Box 90129, Durham, NC 27708
308 Research Drive D310, Box 90129, Durham, NC 27708

Overview


Michael Reiter is a James B. Duke Distinguished Professor of Computer Science and Electrical & Computer Engineering at Duke University.  His previous positions include 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.

Current Appointments & Affiliations


James B. Duke Distinguished Professor of Computer Science · 2021 - Present Computer Science, Trinity College of Arts & Sciences
Professor of Computer Science · 2021 - Present Computer Science, Trinity College of Arts & Sciences
Professor of Electrical and Computer Engineering · 2021 - Present Electrical and Computer Engineering, Pratt School of Engineering

In the News


Published November 12, 2024
AI is the Biggest Cybersecurity Question Mark. Is It Also the Answer?
Published February 9, 2022
The Growing Role of Computation in Science
Published June 28, 2021
Duke Awards 22 Distinguished Professorships

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Recent Publications


Random Beacons in Monte Carlo: Efficient Asynchronous Random Beacon without Threshold Cryptography

Conference CCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security · December 9, 2024 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, wi ... Full text Cite

A General Framework for Data-Use Auditing of ML Models

Conference CCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security · December 9, 2024 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 gene ... Full text Cite

PG: Byzantine Fault-Tolerant and Privacy-Preserving Sensor Fusion With Guaranteed Output Delivery

Conference CCS 2024 - Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security · December 9, 2024 We design and implement PG, a Byzantine fault-tolerant and privacy-preserving multi-sensor fusion system. PG is flexible and extensible, supporting a variety of fusion algorithms and application scenarios. On the theoretical side, PG develops and unifies t ... Full text Cite
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Education, Training & Certifications


Cornell University · 1993 Ph.D.

External Links


Website