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A Mobile Crowd Sensing Framework for Suspect Investigation: An Objectivity Analysis and De-Identification Approach
Computer Science and Information Systems, Tome 17 (2020) no. 1 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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The ubiquity of mobile devices and their advanced features have increased the use of crowdsourcing in many areas, such as the mobility in the smart cities. With the advent of high-quality sensors on smartphones, online communities can easily collect and share information. These information are of great importance for the institutions, which must analyze the facts by facilitating the data collecting on crimes and criminals, for example. This paper proposes an approach to develop a crowdsensing framework allowing a wider collaboration between the citizens and the authorities. In addition, this framework takes advantage of an objectivity analysis to ensure the participants’ credibility and the information reliability, as law enforcement is often affected by unreliable and poor quality data. In addition, the proposed framework ensures the protection of users' private data through a de-identification process. Experimental results show that the proposed framework is an interesting tool to improve the quality of crowdsensing information in a government context.
Keywords: crowdsourcing, crowdsensing, law enforcement, objectivity analysis, de-identification 
@article{CSIS_2020_17_1_a13,
     author = {ElAlaoui ElAbdallaoui Hasna and ElFazziki Abdelaziz and Ennaji Fatima Zohra and Sadgal Mohamed},
     title = {A {Mobile} {Crowd} {Sensing} {Framework} for {Suspect} {Investigation:} {An} {Objectivity} {Analysis} and {De-Identification} {Approach}},
     journal = {Computer Science and Information Systems},
     year = {2020},
     volume = {17},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2020_17_1_a13/}
}
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AU  - Ennaji Fatima Zohra
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%J Computer Science and Information Systems
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ElAlaoui ElAbdallaoui Hasna; ElFazziki Abdelaziz; Ennaji Fatima Zohra; Sadgal Mohamed. A Mobile Crowd Sensing Framework for Suspect Investigation: An Objectivity Analysis and De-Identification Approach. Computer Science and Information Systems, Tome 17 (2020) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2020_17_1_a13/