抗背景知识攻击的顺序数据发布中的隐私保护
Preserving Privacy in Sequential Data Release against Background Knowledge Attacks
摘要：
每天都有大量包含个人和敏感信息之间关联的事务数据流入数据存储。示例包括Web查询、信用卡交易、医疗检查记录、交通数据库记录。将这些数据连续发布给合作机构或数据分析中心是常见的情况。在本文中，我们表明，在大多数领域中，与同一个人在不同的版本中的敏感值之间的相关性可以很容易地挖掘，并用于侵犯用户的隐私，由对手观察多个数据发布。我们提供了一个正式的隐私攻击模型的基础上，这个顺序的背景知识，以及对不同的个人的敏感值的概率分布的背景知识。我们展示了如何连续的背景知识，实际上可以获得的对手，并用于识别与个人相关的敏感值的高置信度。提出了一种基于Jensen-Shannon发散度的防御算法，实验结果表明，该方法相对于其他方法具有更好的性能。据我们所知，这是第一个工作，系统地研究了顺序背景知识的作用，在串行发布的交易数据。
英文摘要：
A large amount of transaction data containing associations between individuals and sensitive information flows everyday into data stores. Examples include web queries, credit card transactions, medical exam records, transit database records. The serial release of these data to partner institutions or data analysis centers is a common situation. In this paper we show that, in most domains, correlations among sensitive values associated to the same individuals in different releases can be easily mined, and used to violate users' privacy by adversaries observing multiple data releases. We provide a formal model for privacy attacks based on this sequential background knowledge, as well as on background knowledge on the probability distribution of sensitive values over different individuals. We show how sequential background knowledge can be actually obtained by an adversary, and used to identify with high confidence the sensitive values associated with an individual. A defense algorithm based on Jensen-Shannon divergence is proposed, and extensive experiments show the superiority of the proposed technique with respect to other applicable solutions. To the best of our knowledge, this is the first work that systematically investigates the role of sequential background knowledge in serial release of transaction data.
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。
