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Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion models emerging as a promising avenue.
Statlog (German Credit Data)
Hans Hofmann · 1994
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Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid
Ron Kohavi et al · 1996
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k-anonymity: A model for protecting privacy
Latanya Sweeney · 2002
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Approximation algorithms for k-anonymity
Gagan Aggarwal, Tomas Feder, Krishnaram Kenthapadi, Rajeev Motwani, Rina Panigrahy, Dilys Thomas, and An Zhu · 2005
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Incognito: Efficient full-domain k-anonymity
Kristen LeFevre, David J DeWitt, and Raghu Ramakrishnan · 2005
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Random projection-based multiplicative data perturbation for privacy preserving distributed data mining
Kun Liu, Hillol Kargupta, and Jessica Ryan · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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t-closeness: Privacy beyond k-anonymity and l-diversity
Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian · 2006
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No longer de-identified
Amy L McGuire and Richard A Gibbs · 2006
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Privacy protection: p-sensitive k-anonymity property
Traian Marius Truta and Bindu Vinay · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
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l-diversity: Privacy beyond k-anonymity
Ashwin Machanavajjhala, Daniel Kifer, Johannes Gehrke, and Muthuramakrishnan Venkitasubramaniam · 2007
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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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A hilbert space embedding for distributions
Alex Smola, Arthur Gretton, Le Song, and Bernhard Schölkopf · 2007
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Towards identity anonymization on graphs
Kun Liu and Evimaria Terzi · 2008
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Privacy: Theory meets practice on the map
Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, and Lars Vilhuber · 2008
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Differential privacy with compression
Shuheng Zhou, Katrina Ligett, and Larry Wasserman · 2009
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Differentially private publication of sparse data
Graham Cormode, Magda Procopiuc, Divesh Srivastava, and Thanh TL Tran · 2011
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Differentially private data cubes: optimizing noise sources and consistency
Bolin Ding, Marianne Winslett, Jiawei Han, and Zhenhui Li · 2011
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A systematic review of re-identification attacks on health data
Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, and Bradley Malin · 2011
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Sharing graphs using differentially private graph models
Alessandra Sala, Xiaohan Zhao, Christo Wilson, Haitao Zheng, and Ben Y Zhao · 2011
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Differentially private spatial decompositions
Graham Cormode, Cecilia Procopiuc, Divesh Srivastava, Entong Shen, and Ting Yu · 2012
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
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Privacy via the johnson-lindenstrauss transform
Krishnaram Kenthapadi, Aleksandra Korolova, Ilya Mironov, and Nina Mishra · 2012
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Fast identity anonymization on graphs
Xuesong Lu, Yi Song, and Stéphane Bressan · 2012
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A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
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A taxonomy of privacy-preserving record linkage techniques
Dinusha Vatsalan, Peter Christen, and Vassilios S Verykios · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Differential privacy and machine learning: a survey and review
Zhanglong Ji, Zachary C Lipton, and Charles Elkan · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Personalized medicine: time for one-person trials
Nicholas J Schork · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation
August DuMont Schütte, Jürgen Hetzel, Sergios Gatidis, Tobias Hepp, Benedikt Dietz, Stefan Bauer, and Patrick Schwab · 2021
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Dp-merf: Differentially private mean embeddings with randomfeatures for practical privacy-preserving data generation
Frederik Harder, Kamil Adamczewski, and Mijung Park · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Publishing attributed social graphs with formal privacy guarantees
Zach Jorgensen, Ting Yu, and Graham Cormode · 2016
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Cited alongside, same era.
A survey of evolution in predictive models and impacting factors in customer churn
Mehreen Ahmed, Hammad Afzal, Awais Majeed, and Behram Khan · 2017
Cited alongside, same era.
Generating multi-label discrete patient records using generative adversarial networks
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F Stewart, and Jimeng Sun · 2017
Cited alongside, same era.
Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
Cited alongside, same era.
Ali Jahanian, Xavier Puig, Yonglong Tian, and Phillip Isola · 2021
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Private graph data release: A survey
Yang Li, Michael Purcell, Thierry Rakotoarivelo, David Smith, Thilina Ranbaduge, and Kee Siong Ng · 2021
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On the privacy properties of gan-generated samples
Zinan Lin, Vyas Sekar, and Giulia Fanti · 2021
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A review on graph neural network methods in financial applications
Jianian Wang, Sheng Zhang, Yanghua Xiao, and Rui Song · 2021
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A survey on differential privacy with machine learning and future outlook
Samah Baraheem and Zhongmei Yao · 2022
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Deep neural networks and tabular data: A survey
Vadim Borisov, Tobias Leemann, Kathrin Seßler, Johannes Haug, Martin Pawelczyk, and Gjergji Kasneci · 2022
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A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
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The privacy onion effect: Memorization is relative
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot, Andreas Terzis, and Florian Tramer · 2022
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Diffusion models for document synthesis
Andrew Carr · 2022
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Differentially private diffusion models
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis · 2022
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Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 2022
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Diffusion models for graphs benefit from discrete state spaces
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin, and Roger Wattenhofer · 2022
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2022
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Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Membership inference attacks against text-to-image generation models
Yixin Wu, Ning Yu, Zheng Li, Michael Backes, and Yang Zhang · 2022
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Extracting training data from diffusion models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Differentially private diffusion models generate useful synthetic images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L Smith, Olivia Wiles, and Borja Balle · 2023
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Membership inference of diffusion models
Hailong Hu and Jun Pang · 2023
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Private graph data release: A survey
Yang Li, Michael Purcell, Thierry Rakotoarivelo, David Smith, Thilina Ranbaduge, and Kee Siong Ng · 2023
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A reparameterized discrete diffusion model for text generation
Lin Zheng, Jianbo Yuan, Lei Yu, and Lingpeng Kong · 2023
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Synthetic data applications in finance
Vamsi K. Potluru, Daniel Borrajo, Andrea Coletta, Niccolò Dalmasso, Yousef El-Laham, Elizabeth Fons, Mohsen Ghassemi, Sriram Gopalakrishnan, Vikesh Gosai, Eleonora Kreačić, Ganapathy Mani, Saheed Obitayo, Deepak Paramanand, Natraj Raman, Mikhail Solonin, Srijan Sood, Svitlana Vyetrenko, Haibei Zhu, Manuela Veloso, and Tucker Balch · 2024
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