文章基本信息

标题：Improved Recommender for Location Privacy Preferences
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作者：Anas A. Hadi ; Jonathan Cazalas
期刊名称：Computer and Information Science
印刷版ISSN：1913-8989
电子版ISSN：1913-8997
出版年度：2015
卷号：8
期号：4
页码：64
DOI：10.5539/cis.v8n4p64
出版社：Canadian Center of Science and Education
摘要：Location-based services are one of the fastest growing technologies. Millions of users are using these services and sharing their locations using their smart devices. The popularity of using such applications, while enabling others to access user’s location, brings with it many privacy issues. The user has the ability to set his location privacy preferences manually. Many users face difficulties in order to set their preferences in the proper way. One solution is to use machine learning based methods to predict location privacy preferences automatically. These models suffer from degraded performance when there is no sufficient training data. Another solution is to make the decision for the intended user, depending on the collected opinions from similar users. User-User Collaborative Filtering (CF) is an example within this category. In this paper, we will introduce an improved machine learning based predictor. The results show significant improvements in the performance. The accuracy was improved from 75.30% up to 84.82%, while the privacy leak was reduced from 11.75% up to 7.65%. We also introduced an integrated model which combines both machine learning based methods and collaborative filtering based methods in order to get the advantages from both of them.