Title

A Location-Privacy Approach for Continuous Queries

Abstract

With the prevalence of smartphones, mobile apps have become more and more popular. However, many mobile apps request location information of the user. If there is nothing in place for location privacy, these mobile app users are in great risk of being tracked by malicious parties. Although the location privacy problem has been studied extensively by resorting to a third-party location anonymizer, there is very little work that allows the users to fully control the disclosure of their data using their smartphones alone. In this paper, we propose a novel Android App called MoveWithMe which automatically generates mocking locations. Most importantly, these mocking locations are not random like those generated by original Android location mocking function. The proposed MoveWithMe app generates k traces of mocking locations and ensures that each trace looks like a trace of a real human and each trace is semantically different from the real user’s trace.

Meeting Name

22nd ACM Symposium on Access Control Models and Technologies, SACMAT 2017 (2017: Jun. 21-23, Indianapolis, IN)

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Comments

This work is supported by the National Science Foundation under Grant No: DGE-1433659.

Document Type

Poster

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 The Authors, All rights reserved.

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