With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading and pricing of multiple correlated queries on private web browsing history data at the same time. We propose CTRADE , which is a novel online data CommodiTization fRamework for trAding multiple correlateD queriEs over private data. CTRADE first devises a modified matrix mechanism to perturb query answers. It especially quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. CTRADE then proposes an ellipsoid-based query pricing mechanism according to a given linear market value model, which exploits the features of the ellipsoid to explore and exploit the close-optimal dynamic price at each round. In particular, the proposed mechanism produces a low cumulative regret, which is quadratic in the dimension of the feature vector and logarithmic in the number of total rounds. Through real-data based experiments, our analysis and evaluation results demonstrate that CTRADE balances total error and privacy preferences well within acceptable running time, indeed produces a convergent cumulative regret with more rounds, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.

中文翻译：

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相关查询中私有数据的在线定价和交易

随着隐私数据的商品化，考虑到用户隐私保护的数据交易成为一个引人入胜的研究课题。当有针对性的广告利用时，私人网络浏览历史的交易为数据消费者带来了巨大的经济价值。而这些私有数据的在线定价，进一步有助于实现更真实的数据交易。在本文中，我们同时研究了对私人网页浏览历史数据的多个相关查询的交易和定价。我们建议CTRADE ，这是一种新颖的在线数据商品化框架，用于在私有数据上交易多个相关查询。 CTRADE 首先设计了一种改进的矩阵机制来扰乱查询答案。它特别量化了经典差分隐私松弛下的隐私损失和新设计的具有松弛矩阵敏感性的机制，并以令人满意的方式进一步补偿数据所有者的多样化隐私损失。CTRADE 然后根据给定的线性市场价值模型提出了基于椭球的查询定价机制，该机制利用椭球的特征来探索和利用每一轮的最接近最优的动态价格。特别是，所提出的机制产生了低累积遗憾，它在特征向量的维度上是二次的，在总轮数上是对数的。通过基于真实数据的实验，我们的分析和评估结果表明CTRADE 在可接受的运行时间内很好地平衡了总误差和隐私偏好，确实产生了更多轮次的收敛累积遗憾，并且还实现了预算平衡、个体理性和真实性的所有理想经济属性。