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Updated: Sep 12, 2025

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
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Secure latent Dirichlet allocation.

Thijs Veugen1,2, Vincent Dunning1, Michiel Marcus1

  • 1Unit ICT, Strategy and Policy, TNO, The Hague, Netherlands.

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|August 8, 2025
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Summary
This summary is machine-generated.

This study introduces a secure, decentralized method for training Latent Dirichlet Allocation (LDA) topic models without sharing sensitive documents. The privacy-preserving approach achieves similar accuracy to centralized methods.

Keywords:
Paillier crypto systemShamir secret sharinglatent Dirichlet allocationsecure multi-party computationtopic modelling

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Area of Science:

  • Computer Science
  • Data Science
  • Cryptography

Background:

  • Topic modeling, including Latent Dirichlet Allocation (LDA), is crucial for document analysis.
  • Centralized topic modeling requires access to all documents, posing privacy risks for sensitive data.
  • Decentralized approaches are needed to train models on distributed, private datasets.

Purpose of the Study:

  • To develop a novel, decentralized approach for training LDA models securely.
  • To preserve data privacy using advanced privacy-enhancing technologies.
  • To introduce new cryptographic primitives for secure computation.

Main Methods:

  • A decentralized protocol for training LDA models without data sharing.
  • Integration of privacy-enhancing technologies for secure computation.
  • Development of cryptographic methods for converting between secret-shared and homomorphic encryption.
  • Creation of a method for drawing random numbers from a finite set with secret weights.

Main Results:

  • The decentralized LDA protocol achieves comparable accuracy to traditional centralized methods.
  • The solution demonstrates linear scalability with the number of words and topics.
  • Training a model with 5 topics and 3000 words takes approximately 16 hours using 1024-bit Paillier keys.

Conclusions:

  • A secure and privacy-preserving decentralized LDA training method is feasible.
  • The proposed cryptographic building blocks have independent applications in secure computation.
  • This approach enables collaborative topic modeling on sensitive, distributed datasets.