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.

翻译：避免披露(DA)系统用于保护数据的保密性,同时允许为分析目的分析和传播数据,这些方法,例如细胞抑制、交换和k-匿名,通常使用,可能具有重大的社会和经济影响,然而,缺乏对其隐私和偏见保障的正式分析,本文件提供了一个框架,以弥补这一差距:它建议对这些机制采用不同的私人版本,并获得其隐私界限;此外,该文件在准确和公正处理美国人口普查数据发布和分类任务方面,将这些机制的业绩与传统的差别隐私权机制进行比较,结果显示,与流行的观念相反,传统的差别隐私技术在准确和公平方面可能优于广泛使用的DA机制的私人对应方。