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Computer Science

D-Index
56
Citations
25523
World Ranking
3950
National Ranking
1876

Overview

Arvind Narayanan is affiliated with Princeton University in the United States and focuses primarily on computer science, with substantial contributions across multiple subfields. Their research encompasses artificial intelligence, computer vision and pattern recognition, information systems, sociology and political science, and statistical and nonlinear physics.

The main topics of Narayanan's work include:

  • Explainable Artificial Intelligence (XAI)
  • Privacy, Security, and Data Protection
  • Artificial Intelligence in Healthcare and Education
  • Information and Cyber Security
  • Generative Adversarial Networks and Image Synthesis
  • Ethics and Social Impacts of AI
  • Internet Traffic Analysis and Secure E-voting

They have published extensively, with a strong presence on arXiv (Cornell University) as well as contributions in other venues such as Science, Patterns, Queue, and specialized outlets on robotics and AI safety.

Frequent coauthors include:

  • Sayash Kapoor
  • Rishi Bommasani
  • Percy Liang
  • Shayne Longpre
  • Peter Henderson

Among recent papers, significant examples include:

  • Leakage and the reproducibility crisis in machine-learning-based science, 2023, Patterns
  • Leakage and the Reproducibility Crisis in ML-based Science, 2022, arXiv (Cornell University)
  • Dark Patterns: Past, Present, and Future, 2020, Queue
  • REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets, 2022, International Journal of Computer Vision
  • How large language models can reshape collective intelligence, 2024, Nature Human Behaviour

Narayanan has also contributed to book publications through Princeton University Press, notably multiple editions titled AI Snake Oil released between 2024 and 2025.

Best Publications

  • Semantics derived automatically from language corpora contain human-like biases

    Aylin Caliskan;Joanna J. Bryson;Joanna J. Bryson;Arvind Narayanan

  • Robust De-anonymization of Large Sparse Datasets

    A. Narayanan;V. Shmatikov

  • Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction

    Arvind Narayanan;Joseph Bonneau;Edward Felten;Andrew Miller

  • De-anonymizing Social Networks

    Arvind Narayanan;Vitaly Shmatikov

  • SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies

    Joseph Bonneau;Joseph Bonneau;Joseph Bonneau;Andrew Miller;Jeremy Clark;Arvind Narayanan

  • Online Tracking: A 1-million-site Measurement and Analysis

    Steven Englehardt;Arvind Narayanan

  • The Web Never Forgets: Persistent Tracking Mechanisms in the Wild

    Gunes Acar;Christian Eubank;Steven Englehardt;Marc Juarez

  • Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites

    Arunesh Mathur;Gunes Acar;Michael J. Friedman;Elena Lucherini

  • Adnostic: Privacy Preserving Targeted Advertising.

    Vincent Toubiana;Arvind Narayanan;Dan Boneh;Helen Nissenbaum

  • Fast dictionary attacks on passwords using time-space tradeoff

    Arvind Narayanan;Vitaly Shmatikov

  • Mixcoin: Anonymity for Bitcoin with accountable mixes.

    Joseph Bonneau;Arvind Narayanan;Andrew Miller;Jeremy Clark

  • Routes for breaching and protecting genetic privacy.

    Yaniv Erlich;Arvind Narayanan

  • Myths and fallacies of "Personally Identifiable Information"

    Arvind Narayanan;Vitaly Shmatikov

  • On the Instability of Bitcoin Without the Block Reward

    Miles Carlsten;Harry Kalodner;S. Matthew Weinberg;Arvind Narayanan

  • Location Privacy via Private Proximity Testing.

    Arvind Narayanan;Narendran Thiagarajan;Mugdha Lakhani;Michael Hamburg

  • "You Might Also Like:" Privacy Risks of Collaborative Filtering

    Joseph A. Calandrino;Ann Kilzer;Arvind Narayanan;Edward W. Felten

  • On the Feasibility of Internet-Scale Author Identification

    A. Narayanan;H. Paskov;N. Z. Gong;J. Bethencourt

  • Bitcoin's academic pedigree

    Arvind Narayanan;Jeremy Clark

  • Ten simple rules for responsible big data research

    Matthew Zook;Solon Barocas;danah boyd;Kate Crawford;Kate Crawford

  • How To Break Anonymity of the Netflix Prize Dataset

    Arvind Narayanan;Vitaly Shmatikov

  • Privacy and Security Myths and Fallacies of Personally Identifiable Information

    Arvind Narayanan;Vitaly Shmatikov

Frequent Co-Authors

Edward W. Felten
Edward W. Felten Princeton University
Andrew Miller
Andrew Miller University of Illinois at Urbana-Champaign
Joseph Bonneau
Joseph Bonneau New York University
Vitaly Shmatikov
Vitaly Shmatikov Cornell University
C. Pandu Rangan
C. Pandu Rangan Indian Institute of Technology Madras
Nick Feamster
Nick Feamster University of Chicago
Helen Nissenbaum
Helen Nissenbaum Cornell University
Dan Boneh
Dan Boneh Stanford University
Peter Bailis
Peter Bailis Stanford University
Lincoln Stein
Lincoln Stein Ontario Institute for Cancer Research

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