Federated topic discovery: A semantic consistent approach

• Main Authors: SHI, Yexuan, TONG, Yongxin, SU, Zhiyang, JIANG, Di, ZHOU, Zimu, ZHANG, Wenbin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Topic discovery; topic models; private data; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6406; https://ink.library.smu.edu.sg/context/sis_research/article/7409/viewcontent/is21_shi_av.pdf
• Institution: Singapore Management University

General-purpose topic models have widespread industrial applications. Yet high-quality topic modeling is becoming increasingly challenging because accurate models require large amounts of training data typically owned by multiple parties, who are often unwilling to share their sensitive data for collaborative training without guarantees on their data privacy. To enable effective privacy-preserving multiparty topic modeling, we propose a novel federated general-purpose topic model named private and consistent topic discovery (PC-TD). On the one hand, PC-TD seamlessly integrates differential privacy in topic modeling to provide privacy guarantees on sensitive data of different parties. On the other hand, PC-TD exploits multiple sources of semantic consistency information to retain the accuracy of topic modeling while protecting data privacy. We verify the effectiveness of PC-TD on real-life datasets. Experimental results demonstrate its superiority over the state-of-the-art general-purpose topic models.