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Mobile Social Service User Identification Framework Based on Action-Characteristic Data Retention

Authors :
Chen-Yu Li
Chien-Cheng Huang
Feipei Lai
San-Liang Lee
Jingshown Wu
Rong-Chi Chang
Hsiang-Wei Huang
Source :
IEEE Access, Vol 8, Pp 127748-127767 (2020)
Publication Year :
2020
Publisher :
IEEE, 2020.

Abstract

Mobile social services are an indispensable part of our daily lives. These services are also favored by criminals because it is difficult to retrieve communication data from them. In the past, communication data provided by telecommunication carriers usually indicated when, from where, and with whom the communication occurred. Presently, it is difficult for law enforcement agencies and public security departments to obtain information regarding mobile social services. For this reason, these departments have requested Internet access service providers to store data that can be used to identify the user of a mobile social service at any given time. However, many non-government and civil society organizations claim that these practices violate privacy rights; hence, they strongly oppose the retention of the subscribers' data by the government. Currently, the European Union law does not allow “general and indiscriminate retention of traffic data and location data,” except for “targeted” use against “serious crimes.” Under this premise, ensuring the necessary data retention, while reducing the privacy violations and maintaining public security is a challenging task. In this study, a novel identification framework based on different types and action characteristics of mobile social services is proposed. Based on this framework, government agencies do not need to retain general and indiscriminate traffic data, but only data that aid in identification. Thus, this framework substantially reduces the volume of potential targets and improves the probability of correct target identification, ensuring a balance between privacy and public security.

Details

Language :
English
ISSN :
21693536
Volume :
8
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.9d3c4f729200497593477adf1b5e57d3
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2020.3009010