Versatile differentially private learning for general loss functions
适用于一般损失函数的灵活差分隐私学习AI 解读 ↓
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适用于一般损失函数的灵活差分隐私学习
- 研究问题
- 如何设计无需预先指定后续分析任务的隐私数据发布机制，并在一般乃至非光滑损失下开展参数估计与统计推断？
- 方法与思路
- 作者使用零膨胀对称多元 Laplace 噪声，提出 f-LDP 与属性级隐私框架，并在 M 估计中构造双重随机校正损失。
- 材料陈述的贡献
- 摘要称，校正方法产生一致且渐近正态的估计，无需对噪声数据数值积分或求导；另比较三类估计量的隐私效率代价。
- 阅读关注点
- 应核对隐私保证的定义、发布机制的适用条件、校正损失所需随机化，以及效率比较和在线数据情形的具体设定。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明。“最小条件”和分析次数不受限的具体含义未说明，不能推定任意任务均适用。
原文摘要
This paper aims to provide a versatile privacy-preserving release mechanism along with a unified approach for subsequent parameter estimation and statistical inference. We propose a privacy mechanism based on zero-inflated symmetric multivariate Laplace (ZIL) noise, which requires no prior specification of subsequent analysis tasks, allows for general loss functions under minimal conditions, imposes no limit on the number of analyses, and is adaptable to increasing data volume in online scenarios. We derive the trade-off function for the proposed ZIL mechanism, which characterizes its privacy protection level. Furthermore, to formalize the local differential privacy (LDP) property of the ZIL mechanism, we extend the classical ε-LDP to a more general f-LDP framework. To address scenarios where only individual attribute values require protection, we propose attribute-level differential privacy (ADP) and its local version. Within the M-estimation framework, we introduce a novel doubly random (DR) corrected loss for the ZIL mechanism, which yields consistent and asymptotically normal M-estimates under differential privacy constraints. The proposed approach is computationally efficient and does not require numerical integration or differentiation for noisy data. It applies to a broad class of loss functions, including nonsmooth ones. Two alternative estimators for smooth loss are also proposed with asymptotic properties. The cost of privacy in terms of estimation efficiency for these three estimators is evaluated both theoretically and numerically.