In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology, and little success in the big data environment, for example, the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends, such as “Industrie 4.0” and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfies differential privacy constraint to protect location data privacy and maximizes the utility of data and algorithm in Industrial IoT. In view of the high value and low density of location data, we combine the utility with the privacy and build a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.

中文翻译：

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工业物联网大数据差分隐私策略的位置隐私保护


在位置隐私保护的研究中，现有的方法大多基于传统的匿名化、模糊化和密码学技术，在大数据环境下收效甚微，例如传感器网络中包含敏感信息，必须对其进行适当的保护。当前的趋势，例如“工业 4.0”和物联网 (IoT)，会生成、处理和交换大量安全关键和隐私敏感数据，这使得它们成为有吸引力的攻击目标。然而，以往的方法忽视了隐私保护问题，导致隐私侵犯。在本文中，我们提出了一种满足差分隐私约束的位置隐私保护方法，以保护位置数据隐私并最大化工业物联网中数据和算法的效用。鉴于位置数据的高价值和低密度，我们将实用性与隐私性相结合，构建了多级位置信息树模型。此外，利用差分隐私的索引机制，根据树节点访问频率来选择数据。最后，利用拉普拉斯方案对选择数据的访问频率添加噪声。理论分析和实验结果表明，所提出的策略可以在安全性、隐私性和适用性方面取得显着的改进。