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Computer Science

D-Index
74
Citations
28135
World Ranking
1465
National Ranking
762

Overview

Vitaly Shmatikov is affiliated with Cornell University in the United States and has a significant research output primarily in the field of computer science. Their work spans multiple areas including artificial intelligence, information systems, computer vision, and sociology and political science, with a dominant focus on artificial intelligence.

The research topics covered by Vitaly Shmatikov include:

  • Adversarial Robustness in Machine Learning
  • Topic Modeling
  • Natural Language Processing Techniques
  • Anomaly Detection Techniques and Applications
  • Security and Verification in Computing
  • Explainable Artificial Intelligence (XAI)
  • Privacy, Security, and Data Protection

The scientist has published extensively, with publications concentrated in venues such as arXiv affiliated with Cornell University. Other venues include the 2022 IEEE Symposium on Security and Privacy (SP), Proceedings on Privacy Enhancing Technologies, and SSRN Electronic Journal. The research at these venues often addresses emerging challenges in machine learning, security, and privacy.

Recent representative papers include:

  • Salvaging Federated Learning by Local Adaptation (2020, arXiv - Cornell University)
  • Blind Backdoors in Deep Learning Models (2020, arXiv - Cornell University)
  • Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures (2022, IEEE Symposium on Security and Privacy)
  • You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion (2020, arXiv - Cornell University)
  • Abusing Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs (2023, arXiv - Cornell University)

Frequent collaborators in research include Eugene Bagdasaryan, John X. Morris, Roei Schuster, Alexander M. Rush, and Collin Zhang, highlighting a network of coauthors also engaged in topics related to machine learning security and privacy.

Best Publications

  • Membership Inference Attacks Against Machine Learning Models

    Reza Shokri;Marco Stronati;Congzheng Song;Vitaly Shmatikov

  • Robust De-anonymization of Large Sparse Datasets

    A. Narayanan;V. Shmatikov

  • Privacy-Preserving Deep Learning

    Reza Shokri;Vitaly Shmatikov

  • De-anonymizing Social Networks

    Arvind Narayanan;Vitaly Shmatikov

  • Exploiting Unintended Feature Leakage in Collaborative Learning

    Luca Melis;Congzheng Song;Emiliano De Cristofaro;Vitaly Shmatikov

  • How To Backdoor Federated Learning.

    Eugene Bagdasaryan;Andreas Veit;Yiqing Hua;Deborah Estrin

  • Airavat: security and privacy for MapReduce

    Indrajit Roy;Srinath T. V. Setty;Ann Kilzer;Vitaly Shmatikov

  • The most dangerous code in the world: validating SSL certificates in non-browser software

    Martin Georgiev;Subodh Iyengar;Suman Jana;Rishita Anubhai

  • Fast dictionary attacks on passwords using time-space tradeoff

    Arvind Narayanan;Vitaly Shmatikov

  • Myths and fallacies of "Personally Identifiable Information"

    Arvind Narayanan;Vitaly Shmatikov

  • Machine Learning Models that Remember Too Much

    Congzheng Song;Thomas Ristenpart;Vitaly Shmatikov

  • Constraint solving for bounded-process cryptographic protocol analysis

    Jonathan Millen;Vitaly Shmatikov

  • The cost of privacy: destruction of data-mining utility in anonymized data publishing

    Justin Brickell;Vitaly Shmatikov

  • Privacy-preserving graph algorithms in the semi-honest model

    Justin Brickell;Vitaly Shmatikov

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

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

  • Timing Analysis in Low-Latency Mix Networks : Attacks and Defenses

    Vitaly Shmatikov;Ming-Hsiu Wang

  • How To Break Anonymity of the Netflix Prize Dataset

    Arvind Narayanan;Vitaly Shmatikov

  • Finite-state analysis of SSL 3.0

    John C. Mitchell;Vitaly Shmatikov;Ulrich Stern

  • Towards Practical Privacy for Genomic Computation

    S. Jha;L. Kruger;V. Shmatikov

  • Information hiding, anonymity and privacy: a modular approach

    Dominic Hughes;Vitaly Shmatikov

  • Differential Privacy Has Disparate Impact on Model Accuracy

    Eugene Bagdasaryan;Omid Poursaeed;Vitaly Shmatikov

  • Privacy and Security Myths and Fallacies of Personally Identifiable Information

    Arvind Narayanan;Vitaly Shmatikov

Frequent Co-Authors

Suman Jana
Suman Jana Columbia University
Thomas Ristenpart
Thomas Ristenpart Cornell University
John C. Mitchell
John C. Mitchell Stanford University
Arvind Narayanan
Arvind Narayanan Princeton University
Stanislaw Jarecki
Stanislaw Jarecki University of California, Irvine
Reza Shokri
Reza Shokri National University of Singapore
Emmett Witchel
Emmett Witchel The University of Texas at Austin
Gethin Norman
Gethin Norman University of Glasgow
Kathryn S. McKinley
Kathryn S. McKinley Google (United States)
Amir Houmansadr
Amir Houmansadr University of Massachusetts Amherst

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