Providing K-Anonymity in location based services

Author:

Gkoulalas-Divanis Aris1,Kalnis Panos2,Verykios Vassilios S.3

Affiliation:

1. Vanderbilt University, Nashville, TN

2. King Abdullah University of Science & Technology, Jeddah, Saudi Arabia

3. University of Thessaly, Volos, Greece

Abstract

The offering of anonymity in relational databases has attracted a great deal of attention in the database community during the last decade [4]. Among the different solution approaches that have been proposed to tackle this problem, K-anonymity has received increased attention and has been extensively studied in various forms. New forms of data that come into existence, like location data capturing user movement, pave the way for the offering of cutting edge services such as the prevailing Location Based Services (LBSs). Given that these services assume an in-depth knowledge of the mobile users' whereabouts it is certain that the assumed knowledge may breach the privacy of the users. Thus, concrete approaches are necessary to preserve the anonymity of the mobile users when requesting LBSs. In this work, we survey recent advancements for the offering of K-anonymity in LBSs. Most of the approaches that have been proposed heavily depend on a trusted server component -- that acts as an intermediate between the end user and the service provider - to preserve the anonymity of the former entity. Existing approaches are partitioned in three categories: (a) historical K-anonymity, (b) location K-anonymity, and (c) trajectory K-anonymity. In each of these categories we present some of the most prevalentmethodologies that have been proposed and highlight their operation.

Publisher

Association for Computing Machinery (ACM)

Cited by 31 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on the Spatial Big Data Platform Scheme Considering Multi-Dimensional Point Data Processing;2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC);2023-12-08

2. Analysis of Context-Oriented Source Location Privacy Preservation Techniques for Wireless Sensor Networks;SN Computer Science;2023-10-14

3. Hexanonymity: a scalable geo-positioned data clustering algorithm for anonymisation purposes;2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW);2023-07

4. A Trajectory Privacy Protection Publishing Method Based on Trajectory Segment Graph Division;2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta);2022-12

5. Privacy‐preserving enhanced dummy‐generation technique for location‐based services;Concurrency and Computation: Practice and Experience;2022-11-17

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3