Norman Sadeh

About

Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU). He has co-founded and co-directed several innovative graduate programs at CMU, including the Privacy Engineering Program, the PhD Program in Societal Computing, and the MBA track in Technology Strategy and Product Management. His current research interests encompass cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, language technologies, and semantic web technologies. Dr. Sadeh is recognized for his pioneering work on AI-based privacy enhancing technologies, such as privacy assistants, automated privacy compliance tools, and NLP-based privacy enhancing technologies. He has conducted foundational research on modeling people's privacy expectations and preferences, as well as privacy and security nudging. His work has influenced privacy-enhancing solutions at major companies including Apple, Google, and Facebook/Meta, and has informed privacy policy and regulatory activities at agencies like the Federal Trade Commission and the California Office of the Attorney General. He is also the lead designer of CMU's Privacy Infrastructure for the Internet of Things (IoT). Norman Sadeh is a successful entrepreneur, having been the founding CEO and chairman of Wombat Security Technologies, a company that defined the user-oriented cybersecurity market and was acquired by Proofpoint in 2018.…

Research topics

• Computer Science
• World Wide Web
• Internet privacy
• Computer Security
• Sociology
• Psychology
• Data science
• Human–computer interaction
• Advertising
• Regional science

Selected publications

• The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City

  Proceedings of the International AAAI Conference on Web and Social Media · 2021 · 432 citations

  Senior authorCorresponding

  Studying the social dynamics of a city on a large scale has tra- ditionally been a challenging endeavor, requiring long hours of observation and interviews, usually resulting in only a par- tial depiction of reality. At the same time, the boundaries of municipal organizational units, such as neighborhoods and districts, are largely statically defined by the city government and do not always reflect the character of life in these ar- eas. To address both difficulties, we introduce a clustering mo…

  DOI

• "It's a scavenger hunt": Usability of Websites' Opt-Out and Data Deletion Choices

  2020 · 107 citations

  We conducted an in-lab user study with 24 participants to explore the usefulness and usability of privacy choices offered by websites. Participants were asked to find and use choices related to email marketing, targeted advertising, or data deletion on a set of nine websites that differed in terms of where and how these choices were presented. They struggled with several aspects of the interaction, such as selecting the correct page from a site's navigation menu and understanding what informatio…

  DOI

• Informing the Design of a Personalized Privacy Assistant for the Internet of Things

  2020 · 101 citations

  Senior authorCorresponding

  Internet of Things (IoT) devices create new ways through which personal data is collected and processed by service providers. Frequently, end users have little awareness of, and even less control over, these devices' data collection. IoT Personalized Privacy Assistants (PPAs) can help overcome this issue by helping users discover and, when available, control the data collection practices of nearby IoT resources. We use semi-structured interviews with 17 participants to explore user perceptions o…

  DOI

• Large language models: a new approach for privacy policy analysis at scale

  Computing · 2024-08-22 · 37 citations

  articleOpen accessSenior author

  Abstract The number and dynamic nature of web sites and mobile applications present regulators and app store operators with significant challenges when it comes to enforcing compliance with applicable privacy and data protection laws. Over the past several years, people have turned to Natural Language Processing (NLP) techniques to automate privacy compliance analysis (e.g., comparing statements in privacy policies with analysis of the code and behavior of mobile apps) and to answer people’s pri…

  PublisherOA PDFDOI

• ATLAS: Automatically Detecting Discrepancies Between Privacy Policies and Privacy Labels

  2023-07-01 · 18 citations

  articleSenior author

  Privacy policies are long, complex documents that end-users seldom read. Privacy labels aim to ameliorate these issues by providing succinct summaries of salient data practices. In December 2020, Apple began requiring that app developers submit privacy labels describing their apps’ data practices. Yet, research suggests that app developers often struggle to do so. In this paper, we automatically identify possible discrepancies between mobile app privacy policies and their privacy labels. Such di…

  PublisherDOI

Recent grants

• CT-T User-Controllable Security and Privacy for Pervasive Computing

  NSF · $1.1M · 2006–2011

• TC: Medium: Collaborative Research: User-Controllable Policy Learning

  NSF · $724k · 2009–2013

• SaTC: CORE: Medium: Collaborative: Contextual Integrity: From Theory to Practice

  NSF · $431k · 2018–2023

Frequent coauthors

• Lorrie Faith Cranor

  Carnegie Mellon University

  50 shared

• Florian Schaub

  University of Michigan–Ann Arbor

  33 shared

• Michael Benisch

  30 shared

• Jason Hong

  Carnegie Mellon University

  30 shared

• Shomir Wilson

  26 shared

• Patrick Gage Kelley

  26 shared

• Alessandro Acquisti

  22 shared

• Fabien Gandon

  21 shared

Labs

• Mobile Commerce LabPI

  Researching new technologies and applying user-centered design principles in the development of solutions to reconcile context-awareness and privacy in mobile and pervasive computing environments.

Education

• Ph.D., Computer Science

  Carnegie Mellon University

  1990

• M.S., Computer Science

  Carnegie Mellon University

  1986

• B.S., Computer Science

  University of Pennsylvania

  1983

Similar researchers at Carnegie Mellon University

• Kathleen M. Carleyh-89
• James Herbslebh-58
• Cleotilde (Coty) Gonzalezh-47