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Secure latent Dirichlet allocation
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AI
DOI：10.3389/fdgth.2025.1610228.png)
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Abstract
En 中文
Topic modelling refers to a popular set of techniques used to discover hidden topics that occur in a collection of documents. These topics can; for example; be used to categorize documents or label text for further processing. One popular topic modelling technique is Latent Dirichlet Allocation (LDA). In topic modelling scenarios; the documents are often assumed to be in one; centralized dataset. However; sometimes documents are held by different parties; and contain privacy- or commercially-sensitive information that cannot be shared. We present a novel; decentralized approach to train an LDA model securely without having to share any information about the content of the documents. We preserve the privacy of the individual parties using a combination of privacy enhancing technologies. Next to the secure LDA protocol; we introduce two new cryptographic building blocks that are of independent interest; a way to efficiently convert between secret-shared- and homomorphic-encrypted data as well as a method to efficiently draw a random number from a finite set with secret weights. We show that our decentralized; privacy preserving LDA solution has a similar accuracy compared to an (insecure) centralised approach. With 1024-bit Paillier keys; a topic model with 5 topics and 3000 words can be trained in around 16 h. Furthermore; we show that the solution scales linearly in the total number of words and the number of topics.
Keywords:
topic modelling
Latent Dirichlet Allocation (LDA)
decentralized learning
privacy-preserving
secure multi-party computation