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Differential Privacy and the Risk-Utility Tradeoff for Multi-dimensional Contingency Tables

机译:多维权变表的差分隐私和风险-实用性权衡

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摘要

The methodology of differential privacy has provided a strong definition of privacy which in some settings, using a mechanism of doubly-exponential noise addition, also allows for extraction of informative statistics from databases. A recent paper extends this approach to the release of a specified set of margins from a multi-way contingency table. Privacy protection in such settings implicitly focuses on small cell counts that might allow for the identification of units that are unique in the database. We explore how well the mechanism works in the context of a series of examples, and the extent to which the proposed differential-privacy mechanism allows for sensible inferences from the released data.
机译:差异隐私的方法论提供了一个强有力的隐私定义,在某些情况下,使用双指数噪声相加机制,还可以从数据库中提取信息统计信息。最近的一篇论文将这种方法扩展到从多向列联表中释放一组指定的边距。在这种设置中,隐私保护隐式地集中在小单元格计数上,这可能允许识别数据库中唯一的单元。我们将在一系列示例的背景下探讨该机制的运行情况,以及所提议的差异性隐私机制在多大程度上可以从已发布的数据中得出明智的推断。

著录项

  • 来源
    《Privacy in statistical databases》|2010年|p.187-199|共13页
  • 会议地点 Corfu(GR);Corfu(GR)
  • 作者单位

    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA,Cylab, and i-Lab, Carnegie Mellon University, Pittsburgh, PA 15213, USA;

    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA,Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA;

    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP311.13;
  • 关键词

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