Building Privacy-preserving Location-based Apps

被引：0
|
作者：
Sweatt, Brian [1 ]
Paradesi, Sharon [2 ]
Liccardi, Ilaria [2 ,3 ]
Kagal, Lalana [2 ]
Pentland, Alex [1 ]
机构：
[1] MIT, Media Lab, Cambridge, MA 02139 USA
[2] MIT, Comp Sci & Artificial Intelligence Lab, Cambridge, MA 02139 USA
[3] INRIA Saclay Ile de France, Orsay, France
来源：
2014 TWELFTH ANNUAL INTERNATIONAL CONFERENCE ON PRIVACY, SECURITY AND TRUST (PST) | 2014年
关键词：
Privacy; Location Data; Social Apps;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Social apps usually require a lot of personal information in order to be tailored to the needs of individual users. However, the inherent social exchange of data exposes a user's personal data to other app users or publicly for anyone to see. In this paper, we present an app that enables users to determine the optimal location and time to meet without exposing their information to other users. We compare this app to other research-based and commercial social apps and show that ours is the only one where the risk of exposure is not present. In order to provide such improved privacy protections, we use openPDS, a decentralized and open-source framework. openPDS enables users to store their data on their own servers and participate in group computations without exposing their raw data.
引用
收藏
页码：27 / 30
页数：4
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