Main Article Content
The amount of retrievable smartphone data is escalating; while some apps on the smartphone are evidently exploiting and leaking users’ data. These phenomena potentially violate privacy and personal data protection laws as various studies have showed that technologies such as artificial intelligence could transform smartphone data into personal data by generating user identification and user profiling. User identification identifies specific users among the data based upon the users’ characteristics and users profiling generates users’ traits (e.g. age and personality) by exploring how data is correlated with personal information. Nevertheless, the comprehensive review papers discussing both of the topics are limited. This paper thus aims to provide a comprehensive review of user identification and user profiling using smartphone data. Compared to the existing review papers, this paper has a broader lens by reviewing the general applications of smartphone data before focusing on smartphone usage data. This paper also discusses some possible data sources that can be used in this research topic.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Big Data Value Association, “Data Protection in the Era of Artificial Intelligence - Trends , existing solutions and recommendations for privacy-preserving technologies,” no. October, 2019.
S. Spiekermann and J. Korunovska, “Towards a value theory for personal data,” J. Inf. Technol., vol. 32, no. 1, pp. 62–84, 2017.
CISSReC, “HASIL SURVEY LEMBAGA RISET CISSReC ‘Tingkat Kesadaran Masyarakat Tentang Keamanan Informasi.,’” 2017.
W. Enck, D. Octeau, P. McDaniel, and S. Chaudhuri, “A study of android application security,” in Proceedings of the 20th USENIX Security Symposium, 2011.
W. Enck et al., “TaintDroid: An information-flow tracking system for realtime privacy monitoring on smartphones,” in Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2010, 2019.
Y. Yang, “Web user behavioral profiling for user identification,” Decis. Support Syst., vol. 49, no. 3, 2010.
F. M. Naini, J. Unnikrishnan, P. Thiran, and M. Vetterli, “Where you are is who you are: User identification by matching statistics,” IEEE Trans. Inf. Forensics Secur., vol. 11, no. 2, 2016.
Y. A. De Montjoye, C. A. Hidalgo, M. Verleysen, and V. D. Blondel, “Unique in the Crowd: The privacy bounds of human mobility,” Sci. Rep., vol. 3, 2013.
T. Stöber, M. Frank, J. Schmitt, and I. Martinovic, “Who do you sync you are? Smartphone fingerprinting via application behaviour,” WiSec 2013 - Proc. 6th ACM Conf. Secur. Priv. Wirel. Mob. Networks, pp. 7–12, 2013.
Z. Tu et al., “Your Apps Give You Away,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 2, no. 3, 2018.
P. Welke, I. Andone, K. B?aszkiewicz, and A. Markowetz, “Differentiating smartphone users by app usage,” in UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016.
J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Cell phone-based biometric identification,” in IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010, 2010.
W. Shi, J. Yang, Y. Jiang, F. Yang, and Y. Xiong, “SenGuard: Passive user identification on smartphones using multiple sensors,” in International Conference on Wireless and Mobile Computing, Networking and Communications, 2011.
L. Rossi, J. Walker, and M. Musolesi, “Spatio-temporal techniques for user identification by means of GPS mobility data,” EPJ Data Sci., vol. 4, no. 1, 2015.
W. Cao, Z. Wu, D. Wang, J. Li, and H. Wu, “Automatic user identification method across heterogeneous mobility data sources,” in 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, 2016.
S. Zahid, M. Shahzad, S. A. Khayam, and M. Farooq, “Keystroke-based user identification on smart phones,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, vol. 5758 LNCS.
L. Sun, Y. Wang, B. Cao, P. S. Yu, W. Srisa-An, and A. D. Leow, “Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, vol. 10536 LNAI.
T. Feng, J. Yang, Z. Yan, E. M. Tapia, and W. Shi, “TIPS: Context-aware implicit user identification using touch screen in uncontrolled environments,” in Proceedings of the 15th Workshop on Mobile Computing Systems and Applications, HotMobile 2014, 2014.
T. Feng et al., “Continuous mobile authentication using touchscreen gestures,” in 2012 IEEE International Conference on Technologies for Homeland Security, HST 2012, 2012.
C. Bo, L. Zhang, X. Y. Li, Q. Huang, and Y. Wang, “SilentSense: Silent user identification via touch and movement behavioral biometrics,” in Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2013.
G. Chittaranjan, B. Jan, and D. Gatica-Perez, “Who’s who with big-five: Analyzing and classifying personality traits with smartphones,” in Proceedings - International Symposium on Wearable Computers, ISWC, 2011.
A. S. de Montjoye, Y. A.; Quoidbach, J.; Robic, F.; & Pentland, “Predicting people personality using novel mobile phone-based metrics,” 2012.
B. Mønsted, A. Mollgaard, and J. Mathiesen, “Phone-based metric as a predictor for basic personality traits,” J. Res. Pers., vol. 74, 2018.
H. Ots, I. Liiv, and D. Tur, “Mobile phone usage data for credit scoring,” in Communications in Computer and Information Science, 2020, vol. 1243 CCIS.
S. Seneviratne, A. Seneviratne, P. Mohapatra, and A. Mahanti, “Your installed apps reveal your gender and more!,” in SPME 2014 - Proceedings of the ACM MobiCom Workshop on Security and Privacy in Mobile Environments, 2014.
S. Seneviratne, A. Seneviratne, P. Mohapatra, and A. Mahanti, “Predicting user traits from a snapshot of apps installed on a smartphone,” ACM SIGMOBILE Mob. Comput. Commun. Rev., vol. 18, no. 2, 2014.
I. M. Pires, N. M. Garcia, N. Pombo, F. Flórez-Revuelta, S. Spinsante, and M. C. Teixeira, “Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices,” Pervasive Mob. Comput., vol. 47, 2018.
R. Gellert and S. Gutwirth, “Beyond accountability, the return to privacy?,” in Managing Privacy Through Accountability, 2012.
S. Zhao et al., “User profiling from their use of smartphone applications: A survey,” Pervasive Mob. Comput., vol. 59, 2019.
M. Alexios, “Smartphone Spying Tools,” University of London, 2018.
A. Lukács, “What Is Privacy? The history and definition of privacy,” Tavaszi Szél 2016 Tanulmánykötet I, Budapest, 15-17 April, pp. 256–265, 2017.
W. Djafar, “Hukum Perlindungan Data Pribadi di Indonesia: Lanskap, Urgensi dan Kebutuhan Pembaruan,” Jakarta, 2019.
The European Parliament and the Council of the European Union, Directive 95/EC of the European parliament and of the council. 1995.
J. Isaak and Mina J. Hanna, “User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection,” Computer (Long. Beach. Calif)., vol. 51, no. 8, pp. 56–59, 2018.
E. Graham-Harrison and C. Cadwalladr, “Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach,” Guard., pp. 1–5, 2018.
M. Theoharidou, A. Mylonas, and D. Gritzalis, “A risk assessment method for smartphones,” in IFIP Advances in Information and Communication Technology, 2012, vol. 376 AICT.
S. Phithakkitnukoon, T. Horanont, G. Di Lorenzo, R. Shibasaki, and C. Ratti, “Activity-Aware Map: Identifying human daily activity pattern using mobile phone data,” in Human Behavior Understandin, 2010.
V. Soto and E. Frías-Martínez, “Automated land use identification using cell-phone records,” MobiSys’11 - Compil. Proc. 9th Int. Conf. Mob. Syst. Appl. Serv. Co-located Work. - HotPlanet’11, pp. 17–22, 2011.
B. C. Csáji et al., “Exploring the mobility of mobile phone users,” Phys. A Stat. Mech. its Appl., vol. 392, no. 6, pp. 1459–1473, 2013.
V. Frias-Martinez and J. Virseda, “Cell Phone Analytics: Scaling Human Behavior Studies into the Millions,” Inf. Technol. Int. Dev., vol. 9, no. 2, pp. 35–50, 2013.
J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, 2020.
UN Global Working Group on Big Data for Official Statistics, “Handbook on the use of Mobile Phone data for Official Statistics,” 2017.
M. D. Chinn and R. W. Fairlie, “ICT Use in the developing World: An Analysis of Differences in Computer and Internet Penetration,” Rev. Int. Econ., vol. 18, no. 1, pp. 153–167, 2010.