Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things

By Chunyong Yin, Jinwen Xi, Ruxia Sun, Jin Wang. IEEE Transactions on Industrial Informatics, 2018

296 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/37e926e875c912c84652545a2c60c117c4ebb921

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Citations in the corpus

Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT (2020, background): “In [6], the authors proposed a protection method that satisfies differential privacy to protect location data privacy, without reducing much utility of data in IIoT.”
Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs (2020, background): “Both the sensitive positions and the private knowledge beneath these locations are investigated and properly handled [18].”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, methodology): “Preserving Consumer Data of IoT [245] Location Privacy in IIoT [246] Distributed Control Systems Sec.”