Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (3): 643-650.DOI: 10.11772/j.issn.1001-9081.2020091463

Special Issue: 第37届CCF中国数据库学术会议(NDBC 2020)

• The 37th CCF National Database Conference (NDBC 2020) • Previous Articles     Next Articles

Personalized privacy protection for spatio-temporal data

LIU Xiangyu, XIA Guoping, XIA Xiufeng, ZONG Chuanyu, ZHU Rui, LI Jiajia   

  1. College of Computer Science, Shenyang Aerospace University, Shenyang Liaoning 110136, China
  • Received:2020-09-07 Revised:2020-10-17 Online:2021-03-10 Published:2020-10-30
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61802268, 61702344), the Key Program of Natural Science Foundation of Liaoning Province (20170520321).


刘向宇, 夏国平, 夏秀峰, 宗传玉, 朱睿, 李佳佳   

  1. 沈阳航空航天大学 计算机学院, 沈阳 110136
  • 通讯作者: 刘向宇
  • 作者简介:刘向宇(1981-),男,辽宁开原人,讲师,博士,CCF会员,主要研究方向:数据隐私保护;夏国平(1995-),男,山东寿光人,硕士研究生,CCF会员,主要研究方向:数据隐私保护;夏秀峰(1964-),男,山东胶南人,教授,博士,CCF会员,主要研究方向:数据库、分布式数据库、数据仓库、数据挖掘;宗传玉(1985-),男,山东潍坊人,讲师,博士,CCF会员,主要研究方向:数据清洗、数据溯源、查询处理优化;朱睿(1982-),男,辽宁沈阳人,副教授,博士,CCF会员,主要研究方向:数据流;李佳佳(1987-),女,辽宁绥中人,副教授,博士,CCF会员,主要研究方向:时空数据库、智能交通。
  • 基金资助:

Abstract: Due to the popularity of smart mobile terminals, sensitive information such as personal location privacy, check-in data privacy and trajectory privacy in the collected spatio-temporal data are easy to be leaked. In the current researches, protection technologies are proposed for the above privacy leakages respectively, and there is not a personalized spatio-temporal data privacy protection method to prevent the above privacy leakages for users. Therefore, a personalized privacy protection model for spatio-temporal data named (p,q,ε)-anonymity and a Personalized Privacy Protection for Spatio-Temporal Data (PPPST) algorithm based on this model were proposed to protect the users' privacy data with personalized settings (location privacy, check-in data privacy and trajectory privacy). The heuristic rules were designed to generalize the spatio-temporal data to ensure the availability of the published data and realize the high availability of spatio-temporal data. In the comparison experiments, the data availability rate of PPPST algorithm is about 4.66% and 15.45% higher than those of Information Data Used through K-anonymity (IDU-K) and Personalized Clique Cloak (PCC) algorithms on average respectively. At the same time, the generalized location search technology was designed to improve the execution efficiency of the algorithm. Experiments and analysis were conducted based on real spatio-temporal data. Experimental results show that PPPST algorithm can effectively protect the privacy of personalized spatio-temporal data.

Key words: spatio-temporal data, privacy protection, personalized, data utility, generalized anonymity

摘要: 智能移动终端的普及导致收集的时空数据中个人位置隐私、签到数据隐私、轨迹隐私等敏感信息容易泄露,且当前研究分别针对上述隐私泄露单独提出保护技术,而没有面向用户给出防止上述隐私泄露的个性化时空数据隐私保护方法。针对这个问题,提出一种面向时空数据的个性化隐私保护模型(pqε)-匿名和基于该模型的个性化时空数据隐私保护(PPPST)算法,从而对用户个性化设置的隐私数据(位置隐私、签到数据隐私和轨迹隐私)加以保护。设计了启发式规则对时空数据进行泛化处理,保证了发布数据的可用性并实现了时空数据的高可用性。对比实验中PPPST算法的数据可用率比个性化信息数据K-匿名(IDU-K)和个性化Clique Cloak(PCC)算法分别平均高约4.66%和15.45%。同时,设计了泛化位置搜索技术来提高算法的执行效率。基于真实时空数据进行实验测试和分析,实验结果表明PPPST算法能有效地保护个性化时空数据隐私。

关键词: 时空数据, 隐私保护, 个性化, 数据可用性, 泛化匿名

CLC Number: