  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.
