局部阻尼：基于局部敏感性的非数字水印的差分隐私
Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity
摘要：
差分隐私是强隐私保证下数据发布的最新形式化定义。在文献中已经提出了多种机制来释放数字查询的输出（例如，拉普拉斯机制和平滑灵敏度机制）。这些机制通过向真实查询的输出添加噪声来保证差分隐私。添加的噪声量通过查询的全局敏感性和局部敏感性的概念来校准，这些概念测量个体的添加或移除对查询的输出的影响。使用局部灵敏度的机制增加的噪声更少，因此具有更准确的答案。然而，尽管已经有一些关于使用全局敏感性来释放非数值查询的输出的通用机制的工作（例如，指数机制），文献缺乏使用局部敏感性来释放非数值查询的输出以减少查询输出中的噪声的通用机制。在这项工作中，我们弥补了这一缺点，并提出了本地阻尼机制。我们适应的概念，局部敏感性的非数值设置，并利用它来设计一个通用的非数值机制。我们提供的指数机制的理论比较，并显示在哪些条件下，本地阻尼机制比指数机制更准确。我们说明了本地阻尼机制的有效性，将其应用到三个不同的问题：（i）百分位数选择问题。我们报告数据库中的第p个元素;（ii）影响节点分析。给定一个影响力度量，我们释放前k个最有影响力的节点，同时保持网络中节点之间关系的隐私;（iii）决策树归纳。我们提供了一个私有的适应ID 3算法建立决策树从给定的表格数据集。
英文摘要：
Differential privacy is the state-of-the-art formal definition for data release under strong privacy guarantees. A variety of mechanisms have been proposed in the literature for releasing the output of numeric queries (e.g., the Laplace mechanism and smooth sensitivity mechanism). Those mechanisms guarantee differential privacy by adding noise to the true query's output. The amount of noise added is calibrated by the notions of global sensitivity and local sensitivity of the query that measure the impact of the addition or removal of an individual on the query's output. Mechanisms that use local sensitivity add less noise and, consequently, have a more accurate answer. However, although there has been some work on generic mechanisms for releasing the output of non-numeric queries using global sensitivity (e.g., the Exponential mechanism), the literature lacks generic mechanisms for releasing the output of non-numeric queries using local sensitivity to reduce the noise in the query's output. In this work, we remedy this shortcoming and present the local dampening mechanism. We adapt the notion of local sensitivity for the non-numeric setting and leverage it to design a generic non-numeric mechanism. We provide theoretical comparisons to the exponential mechanism and show under which conditions the local dampening mechanism is more accurate than the exponential mechanism. We illustrate the effectiveness of the local dampening mechanism by applying it to three diverse problems: (i) percentile selection problem. We report the p-th element in the database; (ii) Influential node analysis. Given an influence metric, we release the top-k most influential nodes while preserving the privacy of the relationship between nodes in the network; (iii) Decision tree induction. We provide a private adaptation to the ID3 algorithm to build decision trees from a given tabular dataset.
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。
