On Privacy Protection of Latent Dirichlet Allocation Model Training
On Privacy Protection of Latent Dirichlet Allocation Model Training
Fangyuan Zhao, Xuebin Ren, Shusen Yang, Xinyu Yang
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 4860-4866.
https://doi.org/10.24963/ijcai.2019/675
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the Collapsed Gibbs Sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms.
Keywords:
Multidisciplinary Topics and Applications: Security and Privacy
Machine Learning: Learning Generative Models
Machine Learning: Recommender Systems
Machine Learning: Probabilistic Machine Learning