Understand
We revisit the problem of $n$-gram extraction in the differential privacy setting.
- In this problem, given a corpus of private text data, the goal is to release as many $n$-grams as possible while preserving user level privacy.
- Extracting $n$-grams is a fundamental subroutine in many NLP applications such as sentence completion, response generation for emails etc.
- The problem also arises in other applications such as sequence mining, and is a generalization of recently studied differentially private set union (DPSU).
Built on
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Differentially private sequential data publication via variable-length n-grams
Rui Chen, Gergely Acs, and Claude Castelluccia · 2012
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Earlier work this paper cites.
Convolutional neural network architectures for matching natural language sentences
Baotian Hu, Zhengdong Lu, Hang Li, and Qingcai Chen · 2014
Earlier work this paper cites.
Dpt: differentially private trajectory synthesis using hierarchical reference systems
Xi He, Graham Cormode, Ashwin Machanavajjhala, Cecilia M Procopiuc, and Divesh Srivastava · 2015
Earlier work this paper cites.
Similar
Differentially private frequent sequence mining via sampling-based candidate pruning
Shengzhi Xu, Sen Su, Xiang Cheng, Zhengyi Li, and Li Xiong · 2015
Cited alongside, same era.
Smart reply: Automated response suggestion for email
Anjuli Kannan, Karol Kurach, Sujith Ravi, Tobias Kaufmann, Andrew Tomkins, Balint Miklos, Greg Corrado, Laszlo Lukacs, Marina Ganea, Peter Young, et al · 2016
Cited alongside, same era.
Differentially private frequent sequence mining
Shengzhi Xu, Xiang Cheng, Sen Su, Ke Xiao, and Li Xiong · 2016
Cited alongside, same era.
Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
Cited alongside, same era.
Privtrie: Effective frequent term discovery under local differential privacy
Ning Wang, Xiaokui Xiao, Yin Yang, Ta Duy Hoang, Hyejin Shin, Junbum Shin, and Ge Yu · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song
Cited in the paper.
Then
Gmail smart compose: Real-time assisted writing
Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, and et al · 2019
Later among the works it cites.
Diversifying reply suggestions using a matching-conditional variational autoencoder
Budhaditya Deb, Peter Bailey, and Milad Shokouhi · 2019
Later among the works it cites.
Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
Later among the works it cites.
Differentially private set union
Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, and Sergey Yekhanin · 2020
Later among the works it cites.
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