Privacy-preserving indoor localization on smartphones
Zeinalipour-Yazdi, Constantinos D.
PublisherInstitute of Electrical and Electronics Engineers Inc.
Source2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
32nd IEEE International Conference on Data Engineering, ICDE 2016
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Predominant smartphone OS localization subsystems currently rely on server-side localization processes, allowing the service provider to know the location of a user at all times. In this paper, we propose an innovative algorithm for protecting users from location tracking by the localization service, without hindering the provisioning of fine-grained location updates on a continuous basis. Our proposed Temporal Vector Map (TVM) algorithm, allows a user to accurately localize by exploiting a k-Anonymity Bloom (kAB) filter and a bestNeighbors generator of camouflaged localization requests, both of which are shown to be resilient to a variety of privacy attacks. We have evaluated our framework using a real prototype developed in Android and Hadoop HBase as well as realistic Wi-Fi traces scaling-up to several GBs. Our study reveals that TVM can offer fine-grained localization in approximately four orders of magnitude less energy and number of messages than competitive approaches. © 2016 IEEE.