A survey of techniques for the identification of mobile phones using the physical fingerprints of the built-in components

The identification of electronic devices through their physical components and properties has many applications in various domains. In recent years, substantial research activity has investigated the feasibility of identification techniques from a theoretical point of view and through extensive experimental studies. In many cases, the research results have shown that a very high identification accuracy can be obtained by exploiting imperfections and small differences in the electronic components, which are called fingerprints in this context. In this paper we survey the techniques for the identification of a specific category of electronic devices, which is extensively used in the everyday life: the mobile phone or smartphone. A mobile phone is usually equipped with various electronic components: the radio frequency front-ends to support wireless communication standards, the high resolution cameras, the Global Navigation Satellite Systems (GNSS) receivers, the Micro-Electro-MEchanical Systems (MEMS) components, the microphone and so on. Each of these components can be identified by different techniques applied to specific features of the components and their digital output. The identification of these components can be used to fingerprint the mobile phone itself. This paper provides a survey of the different techniques, which can be applied to the different components, the types of features which are usually implemented in literature and the classification algorithms. Outstanding challenges and research issues are identified and described. An overview of the potential applications for mobile phone fingerprinting is also presented in this paper. In addition, this paper analyzes the potential privacy risks associated to the tracking of the mobile phone on the basis of its fingerprints and the related mitigation techniques. Finally, this paper identifies research opportunities and potential future trends for mobile phone fingerprinting.

BALDINI Gianmarco;  STERI Gary; 

2017-09-05

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

JRC103234

1553-877X,   

http://ieeexplore.ieee.org/document/7902125/,    https://publications.jrc.ec.europa.eu/repository/handle/JRC103234,   

10.1109/COMST.2017.2694487,