A privacy-enhancing scheme against contextual knowledge-based attacks in location-based services  

作　　者：Jiaxun HUA Yu LIU Yibin SHEN Xiuxia TIAN Yifeng LUO Cheqing JIN 

机构地区：[1]Faculty of Information,East China Normal University,Shanghai 200062,China [2]College of Computer Science&Technology,Shanghai University of Electric Power,Shanghai 200090,China

出　　处：《Frontiers of Computer Science》2020年第3期225-227,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.61532021,U1811264 and U1501252).

摘　　要：1 Introduction and main contributions Location-based services are springing up around us,while leakages of users'privacy are inevitable during services.Even worse,adversaries may analyze intercepted service data,and extract more privacy like health and property.Therefore,privacy preservation is an indispensable guarantee on LBS security.Among the previous approaches to privacy preservation,k-anonymity-based ones have drawn much research attention[1-3].However,some privacy concern will be aroused if these schemes are adopted directly.For instance,Ut issues a query"Find the nearest hotel around me"in such an area as Fig.1(privacy profile k=4).DLS algorithm[2]constructs anonymity set A because these four cells have similar probabilities of being queried in the past.However,experienced adversaries can exclude some cells if they have learned rich contextual knowledge(side information)from historical data,such as features of each cell and LBS users.

关 键 词：LBS services knowledge 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]