Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs
Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs
复制标题
工业物联网中多方的隐私保护数据共享
DOI：
10.1109/jsac.2020.2980802
复制
发表时间：
2020-05-01
影响因子：
16.4
通讯作者：
Cai, Zhipeng
中科院分区：
文献类型：
--
作者：
Zheng, Xu;Cai, Zhipeng
有效的物理数据共享一直在促进工业物联网的功能，这被认为是工业4.0的主要基础之一。这些物理数据在为生产系统的多个组件提供关键信息的同时，也给工人和制造商带来了严重的隐私问题，从而加剧了数据共享的挑战。目前的设计倾向于简化参与者的行为，以便更好地进行理论分析，并且无法正确处理行为更加复杂和相关的IIoT中的挑战。因此，本文提出了一种隐私保护的IIoT数据共享框架，其中多个竞争的数据消费者存在于系统的不同阶段。该框架允许数据贡献者根据请求共享其内容。上传的内容将被干扰，以保持贡献者的敏感状态。在扰动中采用差分隐私来保证隐私保护。然后，数据收集器将处理内容并将其转发给后续的数据消费者。该数据采集器既能获得自身的数据利用，又能在数据中继中获得额外的收益。根据服务提供商是否会进一步处理内容以保留其专有效用，提出了两种算法用于不同场景下的数据共享。这项工作也为两种算法提供了一个全面的考虑隐私，数据效用，带宽效率，支付和数据共享的合理性。最后，对真实数据集的评估表明了所提出方法的有效性，以及工业4.0数据共享的线索。
The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.