Reference : Adaptive Blurring of Sensor Data to balance Privacy and Utility for Ubiquitous Services

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/21077

Title :	Adaptive Blurring of Sensor Data to balance Privacy and Utility for Ubiquitous Services
Language :	English
Author, co-author :	Moawad, Assaad [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Hartmann, Thomas [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Fouquet, François [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Klein, Jacques [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Le Traon, Yves [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Apr-2015
Main document title :	The 30th Annual ACM Symposium on Applied Computing
Publisher :	ACM
Collection and collection volume :	Volume II, Software Platform track
Pages :	2271-2278
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	9781450331968
Event name :	SAC 2015 - The 30th ACM/SIGAPP Symposium On Applied Computing
Event date :	from 13-04-2015 to 17-04-2015
Event place (city) :	Salamanca
Event country :	Spain
Keywords :	[en] Privacy ; Blurring ; Component-based architecture ; Software-platform ; Optimization ; Sensors ; Trade-off ; Masking ; Utility
Abstract :	[en] Given the trend towards mobile computing, the next generation of ubiquitous “smart” services will have to continuously analyze surrounding sensor data. More than ever, such services will rely on data potentially related to personal activities to perform their tasks, e.g. to predict urban traffic or local weather conditions. However, revealing personal data inevitably entails privacy risks, especially when data is shared with high precision and frequency. For example, by analyzing the precise electric consumption data, it can be inferred if a person is currently at home, however this can empower new services such as a smart heating system. Access control (forbid or grant access) or anonymization techniques are not able to deal with such trade-off because whether they completely prohibit access to data or lose source traceability. Blurring techniques, by tuning data quality, offer a wide range of trade-offs between privacy and utility for services. However, the amount of ubiquitous services and their data quality requirements lead to an explosion of possible configurations of blurring algorithms. To manage this complexity, in this paper we propose a platform that automatically adapts (at runtime) blurring components between data owners and data consumers (services). The platform searches the optimal trade-off between service utility and privacy risks using multi-objective evolutionary algorithms to adapt the underlying communication platform. We evaluate our approach on a sensor network gateway and show its suitability in terms of i) effectiveness to find an appropriate solution, ii) efficiency and scalability.
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	I2R-SER-PFN-11COPA > C11/IS/1239572 : COPAINS > 01/01/2012 - 31/12/2014 > LE TRAON Yves
Target :	Researchers ; Professionals ; Students ; General public
Permalink :	http://hdl.handle.net/10993/21077
DOI :	10.1145/2695664.2695855

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