Data Reconstruction: When You See It and When You Don't

24 May 2024

Papers citing "Data Reconstruction: When You See It and When You Don't"

25 / 25 papers shown

Title
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 70 12 0 08 Jul 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 52 40 0 14 Feb 2023
Information Theoretic Lower Bounds for Information Theoretic Upper Bounds Roi Livni 61 16 0 09 Feb 2023
Extracting Training Data from Diffusion Models Nicholas Carlini Jamie Hayes Milad Nasr Matthew Jagielski Vikash Sehwag Florian Tramèr Borja Balle Daphne Ippolito Eric Wallace DiffM 121 606 0 30 Jan 2023
Towards Separating Computational and Statistical Differential Privacy Badih Ghazi Rahul Ilango Pritish Kamath Ravi Kumar Pasin Manurangsi 43 5 0 31 Dec 2022
Measuring Forgetting of Memorized Training Examples Matthew Jagielski Om Thakkar Florian Tramèr Daphne Ippolito Katherine Lee ... Eric Wallace Shuang Song Abhradeep Thakurta Nicolas Papernot Chiyuan Zhang TDI 123 108 0 30 Jun 2022
Reconstructing Training Data from Trained Neural Networks Niv Haim Gal Vardi Gilad Yehudai Ohad Shamir Michal Irani 73 138 0 15 Jun 2022
Strong Memory Lower Bounds for Learning Natural Models Gavin Brown Mark Bun Adam D. Smith 61 12 0 09 Jun 2022
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia William Chan Saurabh Saxena Lala Li Jay Whang ... Raphael Gontijo-Lopes Tim Salimans Jonathan Ho David J Fleet Mohammad Norouzi VLM 404 6,012 0 23 May 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri Laurens van der Maaten 125 54 0 28 Jan 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 84 168 0 13 Jan 2022
Membership Inference Attacks From First Principles Nicholas Carlini Steve Chien Milad Nasr Shuang Song Andreas Terzis Florian Tramèr MIACV MIALM 66 694 0 07 Dec 2021
On the Importance of Difficulty Calibration in Membership Inference Attacks Lauren Watson Chuan Guo Graham Cormode Alex Sablayrolles 82 131 0 15 Nov 2021
On the Complexity of Two-Party Differential Privacy Iftach Haitner N. Mazor Jad Silbak Eliad Tsfadia 30 12 0 17 Aug 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 486 1,917 0 14 Dec 2020
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? Gavin Brown Mark Bun Vitaly Feldman Adam D. Smith Kunal Talwar 292 99 0 11 Dec 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 140 464 0 09 Aug 2020
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion Hongxu Yin Pavlo Molchanov Zhizhong Li J. Álvarez Arun Mallya Derek Hoiem N. Jha Jan Kautz 66 565 0 18 Dec 2019
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference Alexandre Sablayrolles Matthijs Douze Yann Ollivier Cordelia Schmid Hervé Jégou MIACV 64 366 0 29 Aug 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 123 494 0 12 Jun 2019
Towards Formalizing the GDPR's Notion of Singling Out A. Cohen Kobbi Nissim 61 84 0 12 Apr 2019
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks Nicholas Carlini Chang-rui Liu Ulfar Erlingsson Jernej Kos D. Song 136 1,141 0 22 Feb 2018
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 246 4,122 0 18 Oct 2016
Order-Revealing Encryption and the Hardness of Private Learning Mark Bun Mark Zhandry FedML 44 34 0 03 May 2015
Distributed Private Data Analysis: On Simultaneously Solving How and What A. Beimel Kobbi Nissim Eran Omri FedML 108 207 0 14 Mar 2011