dc.contributor.author | Konstantinidis, Andreas | en |
dc.contributor.author | Chatzimilioudis, Georgios | en |
dc.contributor.author | Zeinalipour-Yazdi, Constantinos D. | en |
dc.contributor.author | Mpeis, P. | en |
dc.contributor.author | Pelekis, N. | en |
dc.contributor.author | Theodoridis, Y. | en |
dc.creator | Konstantinidis, Andreas | en |
dc.creator | Chatzimilioudis, Georgios | en |
dc.creator | Zeinalipour-Yazdi, Constantinos D. | en |
dc.creator | Mpeis, P. | en |
dc.creator | Pelekis, N. | en |
dc.creator | Theodoridis, Y. | en |
dc.date.accessioned | 2019-11-13T10:40:45Z | |
dc.date.available | 2019-11-13T10:40:45Z | |
dc.date.issued | 2016 | |
dc.identifier.isbn | 978-1-5090-2019-5 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54280 | |
dc.description.abstract | 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. | en |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en |
dc.source | 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 | en |
dc.source | 32nd IEEE International Conference on Data Engineering, ICDE 2016 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980315213&doi=10.1109%2fICDE.2016.7498379&partnerID=40&md5=6b0a2172a32053a490a7ec12bcd46c16 | |
dc.subject | Data privacy | en |
dc.subject | Privacy preserving | en |
dc.subject | Smartphones | en |
dc.subject | Orders of magnitude | en |
dc.subject | Location | en |
dc.subject | Indoor positioning systems | en |
dc.subject | Localization services | en |
dc.subject | Indoor localization | en |
dc.subject | Innovative algorithms | en |
dc.subject | Location update | en |
dc.subject | Privacy Attacks | en |
dc.subject | Service provider | en |
dc.title | Privacy-preserving indoor localization on smartphones | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/ICDE.2016.7498379 | |
dc.description.startingpage | 1470 | |
dc.description.endingpage | 1471 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: | en |
dc.description.notes | Conference code: 122604</p> | en |
dc.contributor.orcid | Zeinalipour-Yazdi, Constantinos D. [0000-0002-8388-1549] | |
dc.gnosis.orcid | 0000-0002-8388-1549 | |