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2014 Twelfth Annual Conference on Privacy, Security and Trust (PST) (2014)
Toronto, ON, Canada
July 23, 2014 to July 24, 2014
ISBN: 978-1-4799-3502-4
pp: 27-30
Brian Sweatt , Massachusetts Institute of Technology, Cambridge, USA
Sharon Paradesi , Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA
Ilaria Liccardi , Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA
Lalana Kagal , Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA
Alex Pentlandz , Massachusetts Institute of Technology, Cambridge, USA
ABSTRACT
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.
INDEX TERMS
Privacy, Handheld computers, Data privacy, Servers, Sensors, Authorization, Pervasive computing
CITATION

B. Sweatt, S. Paradesi, I. Liccardi, L. Kagal and A. Pentlandz, "Building privacy-preserving location-based apps," 2014 Twelfth Annual Conference on Privacy, Security and Trust (PST), Toronto, ON, Canada, 2014, pp. 27-30.
doi:10.1109/PST.2014.6890920
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