Algorithms that Remember: Model Inversion Attacks and Data Protection Law

12 July 2018

Reuben Binns

Papers citing "Algorithms that Remember: Model Inversion Attacks and Data Protection Law"

31 / 31 papers shown

Title
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review Sonal Allana Mohan Kankanhalli Rozita Dara 35 0 0 05 May 2025
Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Supriya Manna Niladri Sett 183 0 0 30 Dec 2024
A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks Hengzhu Liu Ping Xiong Tianqing Zhu Philip S. Yu 47 6 0 10 Jun 2024
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications Yi Zhang Yuying Zhao Zhaoqing Li Xueqi Cheng Yu-Chiang Frank Wang Olivera Kotevska Philip S. Yu Tyler Derr 31 10 0 31 Aug 2023
AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT Edward H. Lee B. Kelly E. Altinmakas H. Doğan M. Mohammadzadeh ... Faezeh Sazgara S. Wong Michael E. Moseley S. Halabi Kristen W. Yeom FedML OOD 28 1 0 23 Mar 2023
PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients Manas Wadhwa Gagan Raj Gupta Ashutosh Sahu Rahul Saini Vidhi Mittal FedML 24 6 0 19 Mar 2023
Mysterious and Manipulative Black Boxes: A Qualitative Analysis of Perceptions on Recommender Systems Jukka Ruohonen 24 5 0 20 Feb 2023
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection Ege Erdogan Unat Teksen Mehmet Salih Celiktenyildiz Alptekin Kupcu A. E. Cicek 46 4 0 16 Feb 2023
Open RAN Security: Challenges and Opportunities Madhusanka Liyanage An Braeken Shahriar Shahabuddin Pasika Sashmal Ranaweera 39 84 0 03 Dec 2022
Unlearning Graph Classifiers with Limited Data Resources Chao Pan Eli Chien O. Milenkovic MU 27 33 0 06 Nov 2022
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs) E. Jefferson J. Liley Maeve Malone S. Reel Alba Crespi-Boixader ... Christian Cole F. Ritchie A. Daly Simon Rogers Jim Q. Smith 32 7 0 03 Nov 2022
Machine Unlearning of Federated Clusters Chao Pan Jin Sima Saurav Prakash Vishal Rana O. Milenkovic FedML MU 41 25 0 28 Oct 2022
Desiderata for next generation of ML model serving Sherif Akoush Andrei Paleyes A. V. Looveren Clive Cox 38 5 0 26 Oct 2022
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey M. A. Ramírez Song-Kyoo Kim H. A. Hamadi Ernesto Damiani Young-Ji Byon Tae-Yeon Kim C. Cho C. Yeun AAML 25 37 0 21 Feb 2022
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning Ji Gao Sanjam Garg Mohammad Mahmoody Prashant Nalini Vasudevan MIACV AAML 19 22 0 07 Feb 2022
Enhanced Membership Inference Attacks against Machine Learning Models Jiayuan Ye Aadyaa Maddi S. K. Murakonda Vincent Bindschaedler Reza Shokri MIALM MIACV 27 233 0 18 Nov 2021
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models Liam H. Fowl Jonas Geiping W. Czaja Micah Goldblum Tom Goldstein FedML 38 145 0 25 Oct 2021
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning Neil G. Marchant Benjamin I. P. Rubinstein Scott Alfeld MU AAML 28 69 0 17 Sep 2021
Demystifying the Draft EU Artificial Intelligence Act Michael Veale Frederik J. Zuiderveen Borgesius 35 335 0 08 Jul 2021
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 23 71 0 04 Jul 2021
Adaptive Machine Unlearning Varun Gupta Christopher Jung Seth Neel Aaron Roth Saeed Sharifi-Malvajerdi Chris Waites MU 25 174 0 08 Jun 2021
Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity Mathias Parisot Balázs Pejó Dayana Spagnuelo MIACV 27 33 0 27 Apr 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 35 412 0 14 Mar 2021
Anonymizing Machine Learning Models Abigail Goldsteen Gilad Ezov Ron Shmelkin Micha Moffie Ariel Farkash MIACV 19 5 0 26 Jul 2020
A Survey of Privacy Attacks in Machine Learning M. Rigaki Sebastian Garcia PILM AAML 39 213 0 15 Jul 2020
Reducing Risk of Model Inversion Using Privacy-Guided Training Abigail Goldsteen Gilad Ezov Ariel Farkash 30 4 0 29 Jun 2020
Formalizing Data Deletion in the Context of the Right to be Forgotten Sanjam Garg S. Goldwasser Prashant Nalini Vasudevan AILaw MU 43 82 0 25 Feb 2020
Machine Unlearning: Linear Filtration for Logit-based Classifiers Thomas Baumhauer Pascal Schöttle Matthias Zeppelzauer MU 114 130 0 07 Feb 2020
Privacy Attacks on Network Embeddings Michael Ellers Michael Cochez Tobias Schumacher M. Strohmaier Florian Lemmerich AAML 19 12 0 23 Dec 2019
Disparate Vulnerability to Membership Inference Attacks B. Kulynych Mohammad Yaghini Giovanni Cherubin Michael Veale Carmela Troncoso 13 39 0 02 Jun 2019
Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? Sorami Hisamoto Matt Post Kevin Duh MIACV SLR 30 106 0 11 Apr 2019