Abstract
Disclosure avoidance (DA) systems are used to safeguard the confidentiality of data while allowing it to be analyzed and disseminated for analytic purposes. These methods, e.g., cell suppression, swapping, and k-anonymity, are commonly applied and may have significant societal and economic implications. However, a formal analysis of their privacy and bias guarantees has been lacking. This paper presents a framework that addresses this gap: it proposes differentially private versions of these mechanisms and derives their privacy bounds. In addition, the paper compares their performance with traditional differential privacy mechanisms in terms of accuracy and fairness on US Census data release and classification tasks. The results show that, contrary to popular beliefs, traditional differential privacy techniques may be superior in terms of accuracy and fairness to differential private counterparts of widely used DA mechanisms.
Abstract (translated)
Disclosement Avoidance (DA)系统用于保护数据保密性,同时允许对其进行分析和披露以分析目的。这些方法,如细胞抑制、交换和K-匿名化,被广泛采用,可能具有重要社会和经济影响。然而,缺乏对其隐私和偏见保障的正式分析。本文提出了解决这一差距的框架:提出这些机制的不平等隐私版本,并推导其隐私极限。此外,本文在US人口普查数据发布和分类任务的准确性和公平性方面与传统的不平等隐私机制进行比较。结果表明,与流行的 beliefs 相悖,传统不平等隐私技术可能在准确性和公平性方面优于广泛使用的DA机制的不平等隐私对应物。
URL
https://arxiv.org/abs/2301.12204
