Ontology type: schema:ScholarlyArticle
2019-04
AUTHORSImran Memon, Hamid Turab Mirza, Qasim Ali Arain, Hina Memon
ABSTRACTPreserving privacy of vehicle movement is an important challenge in road networks; as trajectory data with spatiotemporal information may reveal much individual information. One of the main threats is revealing history location of vehicle trajectories while it stops and again moves toward the destination. Generally, vehicles stop at mostly two places; the first one is traffic light (signal system)/traffic jam and second is at parking locations such as office, shopping mall, home, hospital etc. While existing works only consider social spots. To cope with this issue, we present a new multiple mix zones de-correlation privacy model in which the degree of de-correlation between parking locations and traffic light/traffic jam places. Further, we consider multiple mix zones method to replace parking locations and traffic light/traffic jam places by de-correlation mix zone region. This paper presents an improved privacy traffic monitoring system for road network applications via a proposed security scheme. Specifically, the proposed model analyzes the monitored scene and deployed mix zones parking location and traffic light/traffic jam places. Our method achieved a high privacy level and anonymity solution for trajectory model; moreover, it also balances the service quality and privacy protection. Finally, we performed experiments on real-world data and showed the effectiveness of our method in comparison to existing methods. More... »
PAGES557-582
http://scigraph.springernature.com/pub.10.1007/s11235-019-00551-1
DOIhttp://dx.doi.org/10.1007/s11235-019-00551-1
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"https://app.dimensions.ai/details/publication/pub.1112283254"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T13:05",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000366_0000000366/records_112059_00000000.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs11235-019-00551-1"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11235-019-00551-1'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11235-019-00551-1'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11235-019-00551-1'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11235-019-00551-1'
This table displays all metadata directly associated to this object as RDF triples.
296 TRIPLES
21 PREDICATES
90 URIs
19 LITERALS
7 BLANK NODES