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Chen Yan, Gao Zhenguo, Wang Haijun, Ouyang Yun, Gou Jin. Node Localization Protocol with Adjustable Privacy Protection Capability[J]. Journal of Computer Research and Development, 2022, 59(9): 2075-2088. DOI: 10.7544/issn1000-1239.20210009
Citation: Chen Yan, Gao Zhenguo, Wang Haijun, Ouyang Yun, Gou Jin. Node Localization Protocol with Adjustable Privacy Protection Capability[J]. Journal of Computer Research and Development, 2022, 59(9): 2075-2088. DOI: 10.7544/issn1000-1239.20210009

Node Localization Protocol with Adjustable Privacy Protection Capability

Funds: This work was supported by the National Natural Science Foundation of China (61671169, 61972166) and the Fund of Key Laboratory of Computer Vision and Machine Learning (Huaqiao University), Fujian Province University (201910).
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  • Published Date: August 31, 2022
  • Privacy-preserving summation (PPS) is a competent node positioning technique with privacy protection capability. However, the traditional PPS requires all participating nodes to generate and transmit a set of random interference matrices, which results in excessive network traffic. To address this issue, we propose the Privacy-preserving summation with k (PPS-k). The PPS-k randomly designates k nodes to generate and transmit random interference matrices. The generation process of the interference matrices can be changed by adjusting the value of k, which makes it more flexible than PPS. The node positioning network is composed of several static anchors that know their own positions. The anchors can communicate with each other and send measurements to the target, to help the target positioning. We define different scenarios according to where the measurements are stored and design PPS-k-based node localization protocols for different scenarios. We also propose a notion that uses the ratio of the number of extra equations to the number of unknown scalars as an indicator to evaluate the privacy protection capability of PPS based technique. Compared with the traditional evaluation criteria, the privacy protection rate eliminates the influence of the dimension of privacy information on the evaluation result when evaluating algorithms privacy protection performance. The simulation results validate the efficiency of the proposed methods with PPS-k in adjusting traffic and privacy protection capability.
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