The trading of social media data has attracted wide research interests over years. In particular, the trading for Web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous data sets, poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer’s utility. In this article, we propose CEATSE, a correlated data trading framework for various kinds of private Web browsing histories. CEATSE first models the correlation among multiple dimensional features, and then generates the optimal feature clustering scheme. Combined with this scheme, CEATSE next incorporates a correlated data perturbation strategy on each feature cluster, in order to balance the privacy-utility tradeoff. It then quantifies each chosen data contributor’s privacy loss on optimal feature clusters. Through real-data-based experiments, our analysis and evaluation results demonstrate CEATSE indeed achieves user privacy protection, the data consumer’s accuracy requirement, and truthfulness, individual rationality as well as budget balance.

中文翻译：

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走向私人网络浏览历史的相关数据交易


多年来，社交媒体数据的交易引起了广泛的研究兴趣。特别是，当网络浏览历史交易应用于有针对性的广告时，可以为数据消费者产生巨大的经济价值。然而，整个浏览历史记录的泄露，即使是以匿名数据集的形式，也会对用户隐私构成巨大威胁。尽管一些现有的解决方案已经研究了社交媒体数据的隐私保护外包，但不幸的是，他们忽略了对数据消费者效用的影响。在本文中，我们提出了 CEATSE，一种用于各种私人 Web 浏览历史的相关数据交易框架。 CEATSE首先对多维特征之间的相关性进行建模，然后生成最优的特征聚类方案。与该方案相结合，CEATSE 接下来在每个特征簇上结合了相关数据扰动策略，以平衡隐私与实用性的权衡。然后，它会量化每个选定的数据贡献者在最佳特征集群上的隐私损失。通过基于真实数据的实验，我们的分析和评估结果表明CEATSE确实实现了用户隐私保护、数据消费者的准确性要求、真实性、个体理性以及预算平衡。