TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Privacy versus Location Accuracy in Opportunistic Wearable Networks

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoInternational Conference on Localization and GNSS (ICL-GNSS)
KustantajaIEEE
Sivut1-6
Sivumäärä6
ISBN (painettu)9781728164557
DOI - pysyväislinkit
TilaJulkaistu - 2 kesäkuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaINTERNATIONAL CONFERENCE ON LOCALIZATION AND GNSS -
Kesto: 1 tammikuuta 1900 → …

Julkaisusarja

NimiInternational Conference on Localization and GNSS
ISSN (elektroninen)2325-0747

Conference

ConferenceINTERNATIONAL CONFERENCE ON LOCALIZATION AND GNSS
Ajanjakso1/01/00 → …

Tiivistelmä

Future wearable devices are expected to increasingly exchange their positioning information with various Location-Based Services (LBSs). Wearable applications can include activity-based health and fitness recommendations, location-based social networking, location-based gamification, among many others. With the growing opportunities for LBSs, it is expected that location privacy concerns will also increase significantly. Particularly, in opportunistic wireless networks based on device-to-device (D2D) connectivity, a user can request a higher level of control over own location privacy, which may result in more flexible permissions granted to wearable devices. This translates into the ability to perform location obfuscation to the desired degree when interacting with other wearables or service providers across the network. In this paper, we argue that specific errors in the disclosed location information feature two components: a measurement error inherent to the localization algorithm used by a wearable device and an intentional (or obfuscation) error that may be based on a trade-off between a particular LBS and a desired location privacy level. This work aims to study the trade-off between positioning accuracy and location information privacy in densely crowded scenarios by introducing two privacy-centric metrics.