按特征敏感度动态调整隐私保护，提升数据利用率。
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
- 基于贝叶斯框架实现特征级隐私量化
- 在均值估计与回归任务中准确率更优
- 适合对隐私与性能平衡有要求的场景
本地差分隐私（LDP）无需信任外部方即可提供强隐私保障，但对所有数据特征施加相同保护，包括低敏感特征，导致下游任务性能下降。为此，我们提出贝叶斯坐标差分隐私（BCDP）框架，实现特征级隐私量化。该方法根据各特征敏感度动态调整隐私保护强度，从而在不损害隐私的前提下提升下游任务性能。我们分析了BCDP的性质，并阐明其与传统非贝叶斯隐私框架的关系。进一步将BCDP应用于私有均值估计和普通最小二乘回归问题，实验表明，相比纯LDP方法，BCDP在保持同等隐私水平的同时显著提升准确率。
原文摘要 · Abstract (English)
Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. However, LDP applies uniform protection to all data features, including less sensitive ones, which degrades performance of downstream tasks. To overcome this limitation, we propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification. This more nuanced approach complements LDP by adjusting privacy protection according to the sensitivity of each feature, enabling improved performance of downstream tasks without compromising privacy. We characterize the properties of BCDP and articulate its connections with standard non-Bayesian privacy frameworks. We further apply our BCDP framework to the problems of private mean estimation and ordinary least-squares regression. The BCDP-based approach obtains improved accuracy compared to a purely LDP-based approach, without compromising on privacy.
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