Abstract

Data trading has attracted increasing attention over the years as a cost-effective business paradigm, probably producing a tremendous amount of economic value. However, the study of query-based trading in the user data market is still in the initial stage. To design a practical user data trading mechanism, we have to consider three major challenges: privacy concern, compensation cost minimization and revenue maximization in a Bayesian environment. By jointly considering these challenges, we propose a profit-maximizing mechanism for user data trading with personalized differential privacy, called READ, which comprised two components, READ-COST for cost minimization and READ-REV for revenue maximization. Especially, READ adopts personalized differential privacy to satisfy each data owner’s diverse privacy preferences. READ-COST greedily selects the most cost-effective data owner to achieve the sub-optimal data query cost. Given this query cost, READ-REV calculates the maximum expected revenue in a Bayesian setting. Through rigorous theoretical analysis and real-data based experiments, we demonstrate that READ achieves all desired properties and approaches the optimal profit.

中文翻译：

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具有个性化差异隐私的基于查询的数据交易的利润最大化机制

摘要

多年来，数据交易作为一种具有成本效益的业务范例已引起越来越多的关注，可能会产生巨大的经济价值。但是，对用户数据市场中基于查询的交易的研究仍处于起步阶段。为了设计一种实用的用户数据交易机制，我们必须考虑三个主要挑战：在贝叶斯环境中的隐私问题，补偿成本最小化和收入最大化。通过共同考虑这些挑战，我们提出了一种具有个性化差异隐私的用户数据交易利润最大化机制，称为READ，该机制包括两个组件：用于成本最小化的READ-COST和用于收益最大化的READ-REV。特别是，请阅读采用个性化的差异隐私，以满足每个数据所有者的不同隐私偏好。READ-COST贪婪地选择最具成本效益的数据所有者，以实现次优的数据查询成本。给定此查询成本，READ-REV将计算贝叶斯设置中的最大预期收入。通过严格的理论分析和基于实际数据的实验，我们证明READ实现了所有所需的属性并接近了最佳利润。