David Choffnes

(he/him/his)

Associate Professor, Executive Director - Cybersecurity and Privacy Institute

David Choffnes
  • Distributed systems
  • Networking
  • Privacy and security
  • Measuring deployed Internet-scale systems (including mobile and IoT)
  • Designing solutions to address problems with reliability, efficiency, and security
  • PhD in Computer Science, Northwestern University
  • MS in Computer Science, Northwestern University
  • BA in Physics and French, Amherst College

David Choffnes is an associate professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston. He is the executive director and a founding member of Northeastern's Cybersecurity and Privacy Institute, which aims to safeguard critical technology by forging partnerships with industry leaders and international academics.

Choffnes aims to understand and improve the privacy, security, performance, and reliability of internet systems. To do so, he measures these systems to understand how well they match existing models and assumptions. In the process of investigating the root causes for violations of those models and assumptions, he often designs, builds, and evaluates new models and systems to allow researchers, users, and policymakers to benefit.

Choffnes has co-authored more than 70 peer-reviewed publications, garnered more than 5,100 citations, and earned funding from the National Science Foundation, the Department of Homeland Security, Google, Comcast, Verizon, Arcep, and the Data Transparency Lab. Choffnes‘ research has been covered in print and on film, including ABC’s 20/20, CBS News, the documentary Harvest, PBS, NPR, the Boston Globe, NBC News, WIRED, Vice News, and Science Magazine. His awards include an NSF CAREER Award, two Google Faculty Awards, two IRTF Applied Networking Research Prizes, a USENIX Security Distinguished Paper Award, an NDSS Distinguished Paper Award, the ACM/CRA Computing Innovation Fellowship, and the Outstanding Dissertation Award in EECS at Northwestern University.

  • Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem

    Citation: Umar Iqbal, Pouneh Nikkhah Bahrami, Rahmadi Trimananda, Hao Cui, Alexander Gamero-Garrido, Daniel J. Dubois, David R. Choffnes, Athina Markopoulou, Franziska Roesner, Zubair Shafiq. (2023). Tracking, Profiling, and Ad Targeting in the Alexa Echo Smart Speaker Ecosystem IMC, 569-583. https://doi.org/10.1145/3618257.3624803

  • In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes

    Citation: Aniketh Girish, Tianrui Hu, Vijay Prakash, Daniel J. Dubois, Srdjan Matic, Danny Yuxing Huang, Serge Egelman, Joel Reardon, Juan Tapiador, David R. Choffnes, Narseo Vallina-Rodriguez. (2023). In the Room Where It Happens: Characterizing Local Communication and Threats in Smart Homes IMC, 437-456. https://doi.org/10.1145/3618257.3624830

  • Internet scale reverse traceroute

    Citation: Kevin Vermeulen, Ege Gürmeriçliler, Ítalo Cunha, David R. Choffnes, Ethan Katz-Bassett. (2022). Internet scale reverse traceroute IMC, 694-715. https://doi.org/10.1145/3517745.3561422

  • A comparative analysis of certificate pinning in Android & iOS

    Citation: Amogh Pradeep, Muhammad Talha Paracha, Protick Bhowmick, Ali Davanian, Abbas Razaghpanah, Taejoong Chung, Martina Lindorfer, Narseo Vallina-Rodriguez, Dave Levin, David R. Choffnes. (2022). A comparative analysis of certificate pinning in Android & iOS IMC, 605-618. https://doi.org/10.1145/3517745.3561439

  • IoTLS: understanding TLS usage in consumer IoT devices

    Citation: Muhammad Talha Paracha, Daniel J. Dubois, Narseo Vallina-Rodriguez, David R. Choffnes. (2021). IoTLS: understanding TLS usage in consumer IoT devices Internet Measurement Conference, 165-178. https://doi.org/10.1145/3487552.3487830

  • AnyOpt: predicting and optimizing IP Anycast performance

    Citation: Xiao Zhang, Tanmoy Sen, Zheyuan Zhang, Tim April, Balakrishnan Chandrasekaran , David R. Choffnes, Bruce M. Maggs, Haiying Shen, Ramesh K. Sitaraman, Xiaowei Yang . (2021). AnyOpt: predicting and optimizing IP Anycast performance SIGCOMM, 447-462. https://doi.org/10.1145/3452296.3472935

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