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Tsinghua Science and Technology 2023, 28(3): 452-463 https://doi.org/10.26599/TST.2022.9010003
Open Access | Issue | Published: 13 December 2022

A Differential Privacy Protection Protocol Based on Location Entropy

Show Author's Information Ping Guo1,  Baopeng Ye2,  Yuling Chen1( ),  Tao Li1,  Yixian Yang3,  Xiaobin Qian4,  Xiaomei Yu5
State Key Laboratory of Public Big Data, School of Computer Science and Technology, Guizhou University, Guiyang 550025, China
Information Technology Innovation Service Center of Guizhou Province, Guiyang 550025, China
School of Cyberspace Security, Beijing University of Posts and Telecommnuications, Beijing 100000, China
Guizhou CoVision Science & Technology Co., Ltd., Guiyang 550025, China
School of Information Science and Engineering, Shandong Normal University, Jinan 250000, China
Keywords:
differential privacy, smart contract, Location-Based Services (LBS), location entropy, privacy protection
Cite this article:
Guo P, Ye B, Chen Y, et al. A Differential Privacy Protection Protocol Based on Location Entropy. Tsinghua Science and Technology, 2023, 28(3): 452-463. https://doi.org/10.26599/TST.2022.9010003
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Abstract Full text About this article

A Location-Based Service (LBS) refers to geolocation-based services that bring both convenience and vulnerability. With an increase in the scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility. To solve the revealing problems in LBS, we propose a differential privacy protection protocol based on location entropy. First, we design an algorithm of the best-assisted user selection for constructing anonymity sets. Second, we employ smart contracts to evaluate the credibility of participants, which ensures the honesty of participants. Moreover, we provide a comprehensive experiment; the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge attacks. Generally, our protocol improves data availability. Particularly, it realizes user-controllable privacy protection, which improves privacy protection and strengthens security.


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About this article

A Differential Privacy Protection Protocol Based on Location Entropy

Show Author's information Ping Guo1, Baopeng Ye2, Yuling Chen1( ), Tao Li1, Yixian Yang3, Xiaobin Qian4, Xiaomei Yu5
State Key Laboratory of Public Big Data, School of Computer Science and Technology, Guizhou University, Guiyang 550025, China
Information Technology Innovation Service Center of Guizhou Province, Guiyang 550025, China
School of Cyberspace Security, Beijing University of Posts and Telecommnuications, Beijing 100000, China
Guizhou CoVision Science & Technology Co., Ltd., Guiyang 550025, China
School of Information Science and Engineering, Shandong Normal University, Jinan 250000, China

Abstract

A Location-Based Service (LBS) refers to geolocation-based services that bring both convenience and vulnerability. With an increase in the scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility. To solve the revealing problems in LBS, we propose a differential privacy protection protocol based on location entropy. First, we design an algorithm of the best-assisted user selection for constructing anonymity sets. Second, we employ smart contracts to evaluate the credibility of participants, which ensures the honesty of participants. Moreover, we provide a comprehensive experiment; the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge attacks. Generally, our protocol improves data availability. Particularly, it realizes user-controllable privacy protection, which improves privacy protection and strengthens security.

Keywords: differential privacy, smart contract, Location-Based Services (LBS), location entropy, privacy protection

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Publication history

Received: 09 November 2021
Revised: 02 December 2021
Accepted: 08 January 2022
Published: 13 December 2022
Issue date: June 2023

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© The author(s) 2023.

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 61962009), Major Scientific and Technological Special Project of Guizhou Province (No. 20183001), Science and Technology Support Plan of Guizhou Province (No. [2020] 2Y011), and the Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2018BDKFJJ005).

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