通过最小化Fisher信息来确保受限加性噪声下的隐私保护
Ensuring Privacy with Constrained Additive Noise by Minimizing Fisher Information
AI总结：
本文研究了在受限加性噪声下如何响应线性或非线性查询以保护数据库中个体条目的隐私，通过最小化Fisher信息矩阵的迹来提升隐私保护，并与差分隐私方法进行比较。
摘要：
本文考虑了在响应线性或非线性查询时，如何通过受限加性噪声来保护数据库中个体条目的隐私问题。为实现隐私保护，查询响应系统性地添加一种加性随机噪声，其支持集是预定义约束集的子集或等于该集。开发了一种使用Fisher信息矩阵迹的倒数作为隐私度量的标准。Cramer-Rao界将数据库条目估计器的方差与引入的隐私度量相关联。计算了最小化Fisher信息迹（作为隐私度量最大化的代理）的概率密度。还提出了动态问题的扩展。最后，将结果与差分隐私方法进行比较。
英文摘要：
The problem of preserving the privacy of individual entries of a database when responding to linear or nonlinear queries with constrained additive noise is considered. For privacy protection, the response to the query is systematically corrupted with an additive random noise whose support is a subset or equal to a pre-defined constraint set. A measure of privacy using the inverse of the trace of the Fisher information matrix is developed. The Cramer-Rao bound relates the variance of any estimator of the database entries to the introduced privacy measure. The probability density that minimizes the trace of the Fisher information (as a proxy for maximizing the measure of privacy) is computed. An extension to dynamic problems is also presented. Finally, the results are compared to the differential privacy methodology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
