对于联邦代表的公用事业泄漏权交易,学习学习
Yuchen Liu1, Onur Günlü2,3, Yuanming Shi1
1School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Entropy (Basel, Switzerland)
|November 26, 2025
概括
联合代表学习 (FRL) 提供隐私优势,但有可能泄露敏感数据. 本研究介绍了一种方法来保护FRL中的特定敏感信息,有效地平衡实用性和隐私.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合表示学习 (FRL) 允许在不共享原始数据的情况下进行分散的数据分析.
- 像差异隐私 (DP) 这样的现有方法提供了一般的保护,但可能会降低性能.
- 存在着保护特定敏感信息的关键需求,同时保持实用性.
研究的目的:
- 调查FRL中敏感属性的信息理论保护.
- 开发一种平衡实用性和敏感信息泄露的方法.
- 为提供可调节的隐私-实用性权衡机制.
主要方法:
- 使用相互信息来量化实用性和敏感信息泄露.
- 提出一种新的FRL方法,将当地DP纳入其中.
- 在限制敏感信息泄漏 (小于 ε) 的情况下最大化实用性.
主要成果:
- 与基线方法相比,拟议方案实现了优越的公用事业泄漏权衡.
- 该方法有效地保护特定的敏感信息 (例如种族).
- 在本地DP中控制噪音水平允许可调节的隐私-实用性权衡.
结论:
- 开发的方法为FRL提供了针对性的隐私方法.
- 这种技术提高了数据的实用性,同时减轻了特定的隐私风险.
- 该方法的可调性性质为各种应用提供了灵活性.
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