Synthetic Text Generation with Differential Privacy: A Simple and
Practical Recipe

Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe

25 October 2022

Huseyin A. Inan

Girish Kumar

Julia McAnallen

Huan Sun

Papers citing "Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe"

19 / 69 papers shown

Title
A Unified View of Differentially Private Deep Generative Modeling Dingfan Chen Raouf Kerkouche Mario Fritz SyDa 21 4 0 27 Sep 2023
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation Xinyu Tang Richard Shin Huseyin A. Inan Andre Manoel Fatemehsadat Mireshghallah Zinan Lin Sivakanth Gopi Janardhan Kulkarni Robert Sim 33 52 0 21 Sep 2023
Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions Dawen Zhang Pamela Finckenberg-Broman Thong Hoang Shidong Pan Zhenchang Xing Mark Staples Xiwei Xu AILaw MU 18 50 0 08 Jul 2023
Harnessing large-language models to generate private synthetic text Alexey Kurakin Natalia Ponomareva Umar Syed Liam MacDermed Andreas Terzis SILM SyDa 20 34 0 02 Jun 2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zi-Han Lin Sivakanth Gopi Janardhan Kulkarni Harsha Nori Sergey Yekhanin 33 36 0 24 May 2023
Privacy Implications of Retrieval-Based Language Models Yangsibo Huang Samyak Gupta Zexuan Zhong K. Li Danqi Chen RALM 25 29 0 24 May 2023
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models Aldo G. Carranza Rezsa Farahani Natalia Ponomareva Alexey Kurakin Matthew Jagielski Milad Nasr SyDa 12 7 0 10 May 2023
Machine Learning for Synthetic Data Generation: A Review Ying-Cheng Lu Minjie Shen Huazheng Wang Xiao Wang Capucine Van Rechem Tianfan Fu Wenqi Wei SyDa 18 138 0 08 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 13 18 0 22 Jan 2023
MAUVE Scores for Generative Models: Theory and Practice Krishna Pillutla Lang Liu John Thickstun Sean Welleck Swabha Swayamdipta Rowan Zellers Sewoong Oh Yejin Choi Zaïd Harchaoui EGVM 23 21 0 30 Dec 2022
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning Yuanhao Xiong Ruochen Wang Minhao Cheng Felix X. Yu Cho-Jui Hsieh FedML DD 36 82 0 20 Jul 2022
Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy Zhiqi Bu J. Mao Shiyun Xu 131 47 0 21 May 2022
Differentially Private Fine-tuning of Language Models Da Yu Saurabh Naik A. Backurs Sivakanth Gopi Huseyin A. Inan ... Y. Lee Andre Manoel Lukas Wutschitz Sergey Yekhanin Huishuai Zhang 134 344 0 13 Oct 2021
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 144 348 0 25 Sep 2021
Deduplicating Training Data Makes Language Models Better Katherine Lee Daphne Ippolito A. Nystrom Chiyuan Zhang Douglas Eck Chris Callison-Burch Nicholas Carlini SyDa 237 588 0 14 Jul 2021
On a Utilitarian Approach to Privacy Preserving Text Generation Zekun Xu Abhinav Aggarwal Oluwaseyi Feyisetan Nathanael Teissier 26 24 0 23 Apr 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 91 110 0 25 Feb 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 267 1,808 0 14 Dec 2020
Private Post-GAN Boosting Marcel Neunhoeffer Zhiwei Steven Wu Cynthia Dwork 110 29 0 23 Jul 2020