推理还是差异：隐私法的规定
Inferential or Differential: Privacy Laws Dictate
摘要：
到目前为止，隐私模型遵循两种范式。第一种范式，在本文中称为推断隐私，重点关注从数据库中的其他记录对有关目标记录的敏感信息进行统计推断所带来的风险。第二种范式被称为差分隐私，关注的是个人在被包括在数据库中时与不被包括在数据库中时所面临的风险。本文的贡献包括两个部分。第一部分对差分隐私进行了批判性分析，得出两个结论：（i）差分隐私机制不提供推理隐私;（ii）关于实现Dalenius隐私目标的不可能性结果[5]是基于图灵机模拟的对手，但人类对手可能会有不同的行为;因此，不可能性结果的实际含义仍然不清楚。这项工作的第二部分是专门解决三个主要缺点，在以前的方法推理隐私：缺乏灵活性，处理变量的敏感性，实用性差，和辅助信息的脆弱性的解决方案。
英文摘要：
So far, privacy models follow two paradigms. The first paradigm, termed inferential privacy in this paper, focuses on the risk due to statistical inference of sensitive information about a target record from other records in the database. The second paradigm, known as differential privacy, focuses on the risk to an individual when included in, versus when not included in, the database. The contribution of this paper consists of two parts. The first part presents a critical analysis on differential privacy with two results: (i) the differential privacy mechanism does not provide inferential privacy, (ii) the impossibility result about achieving Dalenius's privacy goal [5] is based on an adversary simulated by a Turing machine, but a human adversary may behave differently; consequently, the practical implication of the impossibility result remains unclear. The second part of this work is devoted to a solution addressing three major drawbacks in previous approaches to inferential privacy: lack of flexibility for handling variable sensitivity, poor utility, and vulnerability to auxiliary information.
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。
