A Mobile Crowd Sensing Framework for Suspect Investigation: An Objectivity Analysis and De-Identification Approach

ElAlaoui ElAbdallaoui Hasna1, 2, ElFazziki Abdelaziz1, 3, Ennaji Fatima Zohra1, 4 and Sadgal Mohamed1, 5

  1. Computing Systems Engineering Laboratory (LISI)
    Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco

Abstract

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.

Key words

crowdsourcing, crowdsensing, law enforcement, objectivity analysis, de-identification

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS123456789

Publication information

Volume 17, Issue 1 (January 2020)
Year of Publication: 2020
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Hasna, E. E., Abdelaziz, E., Zohra, E. F., Mohamed, S.: A Mobile Crowd Sensing Framework for Suspect Investigation: An Objectivity Analysis and De-Identification Approach. Computer Science and Information Systems, Vol. 17, No. 1, 253-270. (2020), https://doi.org/10.2298/CSIS123456789