安全隐藏的迪里克莱特分配
Thijs Veugen1,2, Vincent Dunning1, Michiel Marcus1
1Unit ICT, Strategy and Policy, TNO, The Hague, Netherlands.
Frontiers in digital health
|August 8, 2025
概括
本研究介绍了一种安全的,去中心化的方法,用于在不共享敏感文件的情况下训练隐性迪里克莱特分配 (LDA) 主题模型. 保护隐私的方法实现了与集中式方法相似的准确性.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 密码学 密码学 密码学 密码学
背景情况:
- 主题建模,包括隐性迪里克莱特分配 (LDA),对于文档分析至关重要.
- 集中主题建模需要访问所有文档,对敏感数据构成隐私风险.
- 需要分散的方法来训练分布式,私有数据集的模型.
研究的目的:
- 开发一种新的,分散的方法,以安全地培训LDA模型.
- 通过使用先进的增强隐私技术来保护数据隐私.
- 为安全计算引入新的加密原体.
主要方法:
- 一个去中心化的协议,用于在没有数据共享的情况下训练LDA模型.
- 整合增强隐私的技术,以实现安全的计算.
- 在秘密共享和同态加密之间进行转换的加密方法的开发.
- 创建了一种方法,可以从具有秘密权重的有限集合中绘制随机数.
主要成果:
- 分散的LDA协议的准确性与传统的集中方法相美.
- 该解决方案通过单词和主题的数量展示了线性可扩展性.
- 训练一个5个主题和3000个单词的模型需要大约16个小时,使用1024位Paillier键.
结论:
- 一种安全和保护隐私的去中心化LDA培训方法是可行的.
- 拟议的加密构建块在安全计算中具有独立的应用.
- 这种方法可以在敏感的分布式数据集上进行协作主题建模.
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