On Privacy Protection of Latent Dirichlet Allocation Model Training
This addresses privacy concerns for users of LDA in text analysis, but it is incremental as it builds on existing differential privacy methods.
The paper tackled privacy risks in training Latent Dirichlet Allocation (LDA) models by developing a privacy monitoring algorithm for centralized datasets and a locally private training algorithm for crowdsourced data, with experimental validation on real-world datasets.
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.