论文标题

隐私感知数据交易

Privacy-aware Data Trading

论文作者

Wang, Shengling, Shi, Lina, Zhang, Junshan, Cheng, Xiuzhen, Yu, Jiguo

论文摘要

数据交易中个人数据泄露的威胁日益增长,这表明迫切需要开发对个人隐私的对策。最先进的工作要么赋予数据收集器的责任,要么仅报告数据的隐私版本。以前方法的基本假设是,数据收集器是可信赖的,并不总是在现实中成立,而后一种方法降低了数据的价值。在本文中,我们从根源研究了隐私泄漏问题。具体来说,我们通过使她与收藏家统治游戏以解决数据交易中的困境,从而扭转了数据提供商的劣势位置。为此,我们提出了嘈杂的零确定性(NSZD)策略,通过调整最初为同时发生游戏设计的经典零确定策略来适应嘈杂的顺序游戏。 NSZD策略可以使数据提供商能够单方面设定数据收集器的预期收益,或者在其与数据收集器的预期收益之间建立正相关关系。两种策略都可以刺激理性的数据收集者诚实行事,从而增强健康的数据交易市场。数值模拟用于检查关键参数的影响以及数据提供商可以成为NSZD播放器的可行区域。最后,我们证明数据收集器不能使用NSZD进一步主导数据市场,以使隐私泄漏恶化。

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.

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