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Profile Privacy Aware Framework for Static objects Participatory Sensing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Dorothy Kalui, Dezheng Zhang

Dorothy Kalui and Dezheng Zhang. Profile Privacy Aware Framework for Static objects Participatory Sensing. International Journal of Computer Applications 153(9):1-9, November 2016. BibTeX

	author = {Dorothy Kalui and Dezheng Zhang},
	title = {Profile Privacy Aware Framework for Static objects Participatory Sensing},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2016},
	volume = {153},
	number = {9},
	month = {Nov},
	year = {2016},
	issn = {0975-8887},
	pages = {1-9},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2016910805},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Mobile devices users in Participatory Sensing Systems (PSS) are required to collect information from their nearby data collection points (DCs). A query normally reveals the identity (id), location, and user profile (e.g., race domain). This information facilitates an adversary PS server to infer over time a comprehensive user location summary with a high degree of precision. Some privacy techniques in PSS have been suggested recently to provide user privacy protection. However, only a few techniques that consider trust in static objects but disregard profile information. For credibility of data, there is scarcely any service, which entails the user to prove that she is at a particular DC point at a certain time. Yet none of the position and time information achieved by nowadays mobile devices is reliable. In this paper, we propose an enhanced K-location privacy-aware framework for static objects in PS system. The experimental results demonstrate in our approach user a high degree of anonymity and reliability of collected data.


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Privacy, Anonymity, location attacks, profile, Visibility