A Model-Agnostic Approach to Differentially Private Topic Mining-Reference-Cited by-同舟云学术

A Model-Agnostic Approach to Differentially Private Topic Mining

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Han¹,Sharma Jayashree¹,Feng Shuya¹,Shu Kai¹,Hong Yuan²

Affiliation:

1. Illinois Institute of Technology, Chicago, IL, USA

2. Illinois Institute of Technology & University of Connecticut, Chicago, IL, USA

Funder

Cisco Research Award

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539417

Reference35 articles.

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2. Three Variants of Differential Privacy: Lossless Conversion and Applications

3. Thee Chanyaswad Alex Dytso H Vincent Poor and Prateek Mittal. 2018. MVG mechanism: Differential privacy under matrix-valued query. In CCS 230--246. Thee Chanyaswad Alex Dytso H Vincent Poor and Prateek Mittal. 2018. MVG mechanism: Differential privacy under matrix-valued query. In CCS 230--246.

4. Martine De Cock Anderson C Nascimento Devin Reich Rafael Dowsley and Ariel Todoki. 2019. Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation. In NeurIPS 3752. Martine De Cock Anderson C Nascimento Devin Reich Rafael Dowsley and Ariel Todoki. 2019. Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation. In NeurIPS 3752.

5. Chris Decarolis Mukul Ram Seyed Esmaeili Yu-Xiang Wang and Furong Huang. 2020. An end-to-end differentially private latent Dirichlet allocation using a spectral algorithm. In ICML 2421--2431. Chris Decarolis Mukul Ram Seyed Esmaeili Yu-Xiang Wang and Furong Huang. 2020. An end-to-end differentially private latent Dirichlet allocation using a spectral algorithm. In ICML 2421--2431.

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