With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders demand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements.

中文翻译：

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在交易特定个人信息的数据集成中实现安全可信度评估和隐私保护

随着电子市场越来越多地采用云服务，利益相关者之间的协作比以往任何时候都更加容易。消费者利益相关者需要来自各种来源的数据来分析趋势和改善客户服务。数据即服务使数据集成能够满足数据消费者的需求。但是，数据必须具有良好的质量和可信度，才能进行准确的分析和有效的决策。此外，数据保管人或提供者必须遵守隐私政策，以避免因违反隐私而受到处罚。为了应对这些挑战，我们提出了双重解决方案：1）我们提出了第一个基于信息熵的信任计算算法 IEB_Trust，允许半信任仲裁员检测不诚实数据提供者的隐蔽行为并为数据混搭选择合格的提供者 2) 我们将 Vickrey-Clarke-Groves (VCG) 拍卖机制用于数据提供者的属性估值数据混搭过程。对真实数据的实验证明，与基于来源的方法相比，我们的方法在限制不诚实的提供者参与数据混搭和提高效率方面的稳健性。此外，我们从他们的信息效用和信任分数中推导出所选提供商的货币份额，这些分数是根据他们的联合隐私要求对集成数据集的差异化私人发布的。