World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
35
Citations
6127
World Ranking
11590
National Ranking
4759

Overview

Amir Houmansadr is affiliated with the University of Massachusetts Amherst in the United States. Their primary field of research is Computer Science, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, and Computer Networks and Communications among other subfields.

The scientist's recent publications include the following:

  • Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning, 2022, 2022 IEEE Symposium on Security and Privacy (SP)
  • Membership Privacy for Machine Learning Models Through Knowledge Transfer, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Improving Deep Learning with Differential Privacy using Gradient Encoding and Denoising, 2020, arXiv (Cornell University)
  • Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture, 2021, arXiv (Cornell University)
  • FINN: Fingerprinting Network Flows using Neural Networks, 2021, Annual Computer Security Applications Conference

Frequent co-authors of Amir Houmansadr include:

  • Dennis Goeckel (11 coauthored works)
  • Ali Naseh (10 coauthored works)
  • Virat Shejwalkar (9 coauthored works)
  • Hossein Pishro-Nik (8 coauthored works)
  • Yuefeng Peng (8 coauthored works)

The main publication venues for their work include:

  • arXiv (Cornell University) with 39 publications
  • Proceedings of the AAAI Conference on Artificial Intelligence with 2 publications
  • IEEE Internet of Things Journal with 2 publications
  • 2022 IEEE Symposium on Security and Privacy (SP) with 1 publication
  • Annual Computer Security Applications Conference with 1 publication

Amir Houmansadr's research covers several major topics, including:

  • Privacy-Preserving Technologies in Data
  • Adversarial Robustness in Machine Learning
  • Internet Traffic Analysis and Secure E-voting
  • Network Security and Intrusion Detection
  • Cryptography and Data Security
  • Advanced Malware Detection Techniques
  • Topic Modeling

The scientist's work primarily focuses on enhancing data privacy and developing secure machine learning techniques. Projects involve investigating poisoning attacks on federated learning, membership privacy methods, and mechanisms for mitigating inference attacks through model architectures. Their contributions span both theoretical and applied aspects within cybersecurity and artificial intelligence domains.

Best Publications

  • Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

    Milad Nasr;Reza Shokri;Amir Houmansadr

  • Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

    Milad Nasr;Reza Shokri;Amir Houmansadr

  • Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning.

    Virat Shejwalkar;Amir Houmansadr

  • Machine Learning with Membership Privacy using Adversarial Regularization

    Milad Nasr;Reza Shokri;Amir Houmansadr

  • Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

    Unknown

  • The Parrot Is Dead: Observing Unobservable Network Communications

    A. Houmansadr;C. Brubaker;V. Shmatikov

  • RAINBOW: A Robust And Invisible Non-Blind Watermark for Network Flows.

    Amir Houmansadr;Negar Kiyavash;Nikita Borisov

  • Cirripede: circumvention infrastructure using router redirection with plausible deniability

    Amir Houmansadr;Giang T.K. Nguyen;Matthew Caesar;Nikita Borisov

  • Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks.

    Milad Nasr;Reza Shokri;Amir Houmansadr

  • Information Hiding in Communication Networks: Fundamentals, Mechanisms, Applications, and Countermeasures

    Wojciech Mazurczyk;Steffen Wendzel;Sebastian Zander;Amir Houmansadr

  • A cloud-based intrusion detection and response system for mobile phones

    Amir Houmansadr;Saman A. Zonouz;Robin Berthier

  • Stegobot: a covert social network botnet

    Shishir Nagaraja;Amir Houmansadr;Pratch Piyawongwisal;Vijit Singh

  • Secloud: A cloud-based comprehensive and lightweight security solution for smartphones

    Saman Zonouz;Amir Houmansadr;Robin Berthier;Nikita Borisov

  • SWIRL: A Scalable Watermark to Detect Correlated Network Flows.

    Amir Houmansadr;Nikita Borisov

  • I want my voice to be heard: IP over Voice-over-IP for unobservable censorship circumvention.

    Amir Houmansadr;Thomas J. Riedl;Nikita Borisov;Andrew C. Singer

  • Multi-flow attacks against network flow watermarking schemes

    Negar Kiyavash;Amir Houmansadr;Nikita Borisov

  • DeepCorr: Strong Flow Correlation Attacks on Tor Using Deep Learning

    Milad Nasr;Alireza Bahramali;Amir Houmansadr

  • CensorSpoofer: asymmetric communication using IP spoofing for censorship-resistant web browsing

    Qiyan Wang;Xun Gong;Giang T.K. Nguyen;Amir Houmansadr

  • Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer.

    Hongyan Chang;Virat Shejwalkar;Reza Shokri;Amir Houmansadr

  • Robust Adversarial Attacks Against DNN-Based Wireless Communication Systems

    Alireza Bahramali;Milad Nasr;Amir Houmansadr;Dennis Goeckel

  • Membership Privacy for Machine Learning Models Through Knowledge Transfer.

    Virat Shejwalkar;Amir Houmansadr

  • Membership Privacy for Machine Learning Models Through Knowledge Transfer

    Virat Shejwalkar;Amir Houmansadr

  • CloudTransport: Using Cloud Storage for Censorship-Resistant Networking

    Chad Brubaker;Chad Brubaker;Amir Houmansadr;Vitaly Shmatikov

  • CoCo: coding-based covert timing channels for network flows

    Amir Houmansadr;Nikita Borisov

  • Compressive Traffic Analysis: A New Paradigm for Scalable Traffic Analysis

    Milad Nasr;Amir Houmansadr;Arya Mazumdar

Frequent Co-Authors

Nikita Borisov
Nikita Borisov University of Illinois at Urbana-Champaign
Dennis Goeckel
Dennis Goeckel University of Massachusetts Amherst
Wojciech Mazurczyk
Wojciech Mazurczyk Warsaw University of Technology
Reza Shokri
Reza Shokri National University of Singapore
Don Towsley
Don Towsley University of Massachusetts Amherst
Vitaly Shmatikov
Vitaly Shmatikov Cornell University
Matthew Caesar
Matthew Caesar University of Illinois at Urbana-Champaign
Nick Feamster
Nick Feamster University of Chicago
Andrew C. Singer
Andrew C. Singer University of Illinois at Urbana-Champaign
Thomas Riedl
Thomas Riedl University of Wuppertal

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