Fetching the paper…
Reading the bibliography…
Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant individuals the right to be forgotten.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Significance level
Robert M Craparo · 2007
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Applied statistics and probability for engineers
Douglas C Montgomery and George C Runger · 2010
Earlier work this paper cites.
The eu proposal for a general data protection regulation and the roots of the ‘right to be forgotten’
Alessandro Mantelero · 2013
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Machine learning models that remember too much
Congzheng Song, Thomas Ristenpart, and Vitaly Shmatikov · 2017
Earlier work this paper cites.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
Earlier work this paper cites.
A guide to the california consumer privacy act of 2018
Lydia de la Torre · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Cited alongside, same era.
Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
Cited alongside, same era.
Reflection backdoor: A natural backdoor attack on deep neural networks
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
Later among the works it cites.
Radioactive data: tracing through training
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, and Hervé Jégou · 2020
Later among the works it cites.
Hidden trigger backdoor attacks
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2020
Later among the works it cites.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Later among the works it cites.
Membership inference attacks on machine learning: A survey
Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S Yu, and Xuyun Zhang · 2021
Later among the works it cites.
Entangled watermarks as a defense against model extraction
Hengrui Jia, Christopher A Choquette-Choo, Varun Chandrasekaran, and Nicolas Papernot · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Mario Fritz, and Michael Backes · 2019
Cited alongside, same era.
Auditing data provenance in text-generation models
Congzheng Song and Vitaly Shmatikov · 2019
Cited alongside, same era.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
Cited alongside, same era.
Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
Cited alongside, same era.
Yiming Li, Baoyuan Wu, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2020
Cited alongside, same era.
Later among the works it cites.
Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, and Tom Goldstein · 2021
Later among the works it cites.
Systematic evaluation of privacy risks of machine learning models
Liwei Song and Prateek Mittal · 2021
Later among the works it cites.
Anti-neuron watermarking: Protecting personal data against unauthorized neural model training
Zihang Zou, Boqing Gong, and Liqiang Wang · 2021
Later among the works it cites.
The ethical application of biometric facial recognition technology
Marcus Smith and Seumas Miller · 2022
Closest in time.