David Choffnes

About

David Choffnes is a Professor and the Executive Director of the Cybersecurity and Privacy Institute at Khoury. His role involves leading interdisciplinary efforts in cybersecurity and privacy, collaborating with Khoury to advance research and education in these fields. The biography emphasizes his leadership position and his association with Khoury, but does not provide additional details about his research focus, background, or key contributions.

Research topics

• Computer Science
• Computer Security
• Internet privacy
• World Wide Web
• Political Science
• Human–computer interaction
• Sociology
• Psychology
• Operating system
• Computer network

Selected publications

• FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic

  2020 · 293 citations

  Mobile-application fingerprinting of network traffic is valuable for many security solutions as it provides insights into the apps active on a network. Unfortunately, existing techniques require prior knowledge of apps to be able to recognize them. However, mobile environments are constantly evolving, i.e., apps are regularly installed, updated, and uninstalled. Therefore, it is infeasible for existing fingerprinting approaches to cover all apps that may appear on a network. Moreover, most mobil…

  DOI

• A Comparative Study of Dark Patterns Across Web and Mobile Modalities

  Proceedings of the ACM on Human-Computer Interaction · 2021 · 109 citations

  Dark patterns are user interface elements that can influence a person's behavior against their intentions or best interests. Prior work identified these patterns in websites and mobile apps, but little is known about how the design of platforms might impact dark pattern manifestations and related human vulnerabilities. In this paper, we conduct a comparative study of mobile application, mobile browser, and web browser versions of 105 popular services to investigate variations in dark patterns ac…

  DOI

• Understanding Dark Patterns in Home IoT Devices

  2023-04-19 · 41 citations

  article

  Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufac…

  PublisherDOI

• Exploring Deceptive Design Patterns in Voice Interfaces

  2022 · 39 citations

  Deceptive design patterns (sometimes called “dark patterns”) are user interface design elements that may trick, deceive, or mislead users into behaviors that often benefit the party implementing the design over the end user. Prior work has taxonomized, investigated, and measured the prevalence of such patterns primarily in visual user interfaces (e.g., on websites). However, as the ubiquity of voice assistants and other voice-assisted technologies increases, we must anticipate how deceptive desi…

  PublisherOA PDFDOI

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

  2023 · 27 citations

  Smart speakers collect voice commands, which can be used to infer sensitive information about users. Given the potential for privacy harms, there is a need for greater transparency and control over the data collected, used, and shared by smart speaker platforms as well as third party skills supported on them. To bridge this gap, we build a framework to measure data collection, usage, and sharing by the smart speaker platforms. We apply our framework to the Amazon smart speaker ecosystem. Our res…

  PublisherOA PDFDOI

Recent grants

• CI-New: Collaborative Research: An Open Platform for Internet Routing Experiments

  NSF · $361k · 2015–2018

• SaTC: Frontiers: Collaborative: Protecting Personal Data Flow on the Internet

  NSF · $1.7M · 2020–2026

• TWC: Small: Efficient Traffic Analysis Resistance for Anonymity Networks

  NSF · $508k · 2016–2020

Frequent coauthors

• Ashwin Rao

  University of Helsinki

  45 shared

• Martina Lindorfer

  TU Wien

  44 shared

• Narseo Vallina-Rodriguez

  43 shared

• Álvaro Feal

  Universidad del Noreste

  38 shared

• Amogh Pradeep

  Boston University

  38 shared

• Julien Gamba

  Universidad Carlos III de Madrid

  37 shared

• Alan Mislove

  Northeastern University

  34 shared

• Fabián E. Bustamante

  Northwestern University

  30 shared

Labs

• Cybersecurity and Privacy InstitutePI

Education

• Ph.D., Computer Science

  Massachusetts Institute of Technology

  1996

• M.S., Computer Science

  Massachusetts Institute of Technology

  1993

• B.S., Computer Science

  University of California, Berkeley

  1989

Similar researchers at Northeastern University

• Zhengzhong Jinh-10