With the proliferation of the digital data economy, digital data is considered as the crude oil in the twenty-first century, and its value is increasing. Keeping pace with this trend, the model of data market trading between data providers and data consumers, is starting to emerge as a process to obtain high-quality personal information in exchange for some compensation. However, the risk of privacy violations caused by personal data analysis hinders data providers' participation in the data market. Differential privacy, a de-facto standard for privacy protection, can solve this problem, but, on the other hand, it deteriorates the data utility. In this paper, we introduce a pricing mechanism that takes into account the trade-off between privacy and accuracy. We propose a method to induce the data provider to accurately report her privacy price and, we optimize it in order to maximize the data consumer's profit within budget constraints. We show formally that the proposed mechanism achieves these properties, and also, validate them experimentally.
翻译:随着数字数据经济的扩展,数字数据被视为21世纪的原油,其价值正在增加。跟上这一趋势,数据提供者和数据消费者之间的数据市场交易模式开始成为获取高质量个人信息以换取某种补偿的一个过程。然而,个人数据分析造成的侵犯隐私的风险妨碍了数据提供者参与数据市场。不同的隐私,即隐私保护的事实上的标准,可以解决这个问题,但另一方面,它恶化了数据的效用。我们在本文件中引入了一种定价机制,考虑到隐私和准确性之间的权衡。我们建议了一种方法,促使数据提供者准确地报告其隐私价格,我们优化这一方法,以便在预算限制范围内最大限度地增加数据消费者的利润。我们正式表明,拟议的机制实现了这些特性,并且实验性地验证了这些特性。