ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2022, Vol. 59 ›› Issue (9): 2075-2088.doi: 10.7544/issn1000-1239.20210009

• 信息安全 • 上一篇    下一篇

隐私保护能力可调的节点定位协议

陈岩,高振国,王海军,欧阳云,缑锦   

  1. (华侨大学计算机科学与技术学院 福建厦门 361021) (计算机视觉与机器学习福建省高校重点实验室(华侨大学) 福建厦门 361021) (20013083005@stu.hqu.edu.cn)
  • 出版日期: 2022-09-01
  • 基金资助: 
    国家自然科学基金项目(61671169,61972166);计算机视觉与机器学习福建省高校重点实验室(华侨大学)基金项目(201910)

Node Localization Protocol with Adjustable Privacy Protection Capability

Chen Yan, Gao Zhenguo, Wang Haijun, Ouyang Yun, Gou Jin   

  1. (College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian 361021) (Key Laboratory of Computer Vision and Machine Learning (Huaqiao University), Fujian Province University, Xiamen, Fujian 361021)
  • Online: 2022-09-01
  • Supported by: 
    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).

摘要: 在由锚节点和目标点组成的节点定位网络中,传统的隐私保护求和(privacy-preserving summation, PPS)算法要求所有参与通信的节点均生成并传输1组干扰矩阵,导致了非必要的通信开销.为打破该局限,提出了k型隐私保护求和(privacy-preserving summation with k, PPS-k)算法,随机指定k个节点生成和传输干扰矩阵,干扰矩阵的生成和传输过程可通过改变k值动态调整.PPS-k兼顾隐私保护能力与通信量限制,具有较高的灵活性.之后,将PPS-k应用于具体的定位场景,提出对应的隐私保护节点定位协议.提出隐私保护率的概念,利用估计其他节点隐私信息所需要的额外方程数与隐私信息中未知量个数之比评估隐私保护能力.与传统的评估标准相比,消除了隐私信息维度对算法隐私保护性能评估结果的影响.仿真结果验证了理论分析的有效性.

关键词: 隐私保护求和, 节点定位, 隐私保护率, 无线传感网络, 隐私保护

Abstract: 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.

Key words: privacy-preserving summation (PPS), node localization, privacy protection rate, wireless sensor network, privacy protection

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