View analysis of personal information leakage and privacy protection in big data era—based on Q method
| DOI | https://doi.org/10.1108/AJIM-05-2021-0144 |
| Published date | 02 November 2021 |
| Date | 02 November 2021 |
| Pages | 901-927 |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
| Author | Lei Huang,Jingyi Zhou,Jiecong Lin,Shengli Deng |
View analysis of personal
information leakage and privacy
protection in big data era—based
on Q method
Lei Huang
Department of Computer Science, City University of Hong Kong,
Kowloon, Hong Kong
Jingyi Zhou
School of Information Management, Wuhan University, Wuhan, China
Jiecong Lin
Department of Computer Science, City University of Hong Kong,
Kowloon, Hong Kong, and
Shengli Deng
School of Information Management, Wuhan University, Wuhan, China
Abstract
Purpose –In the era of big data, people are more likely to pay attention to privacy protection with facing the
risk of personal information leakage while enjoying the convenience brought by big data technology.
Furthermore, people’s views on personal information leakage and privacy protection are varied, playing an
important role in the legal process of personal information protection. Therefore, this paper aims to propose a
semi-qualitative method based framework to reveal the subjective patterns about information leakage and
privacy protection and further provide practical implications for interested party.
Design/methodology/approach –Q method is a semi-qualitative methodology which is designed for
identifying typologies of perspectives. In order to have a comprehensive understanding of users’viewpoints,
this study incorporates LDA & TextRank method and other information extraction technologies to capture the
statements from large-scale literature, app reviews, typical cases and survey interviews, which could be
regarded as the resource of the viewpoints.
Findings –By adopting the Q method that aims for studying subjective thought patterns to identify users’
potential views, the authors have identified three categories of stakeholders’subjectivities: macro-policy
sensitive,trade-offs andpersonal information sensitive, each of which perceives different risk and affordance of
information leakage and importance and urgency of privacy protection. All of the subjectivities of the
respondents reflect the awareness of the issue of information leakage, that is, the interested parties like social
network sites are unable to protect their full personal information, while reflecting varied resistance and
susceptibility of disclosing personal information for big data technology applications.
Originality/value –The findings of this study provide an overview of the subjective patterns on the
information leakage issue. Being the first to incorporate the Q method to study the views of personal
information leakage and privacy protection, the research not only broadens the application field of the
Q method but also enriches the research methods for personal information protection. Besides, the proposed
LDA & TextRank method in this paper alleviates the limitation of statements resource in the Q method.
Keywords Q method, Information leakage, Privacy protection, View, Subjectivity
Paper type Research paper
Q method for
privacy
protection
issue
901
This research is supported in part by National Natural Science Foundation, PR China (Grant No.
71974149) and Wuhan University artificial intelligence project (Grant No. 2020AI021).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-3806.htm
Received 27 May 2021
Revised 18 July 2021
15 September 2021
Accepted 13 October 2021
Aslib Journal of Information
Management
Vol. 74 No. 5, 2022
pp. 901-927
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-05-2021-0144
1. Introduction
Privacy protection in the big data era has become increasingly necessary due to more and
more personal information leakage incidents. The disorder of excessive collection of user
information becomes increasingly severe, leading to worries about the security of accurate
personalized service in the development of big data technology (China Academy of
Information and Communications Technology, 2020). For example, in 2018, Facebook was
embroiled in a data abuse scandal. A British company called Cambridge Analytica has been
revealed to have improperly accessed the data of 87 million Facebook users (The Verge, 2018).
In 2019, Malaysia Airlines was hacked by two employees and millions of passengers’
information was leaked according to A Reuters report (Reuters, 2019). All these incidents
indicate that information leakage frequently occurs with serious consequences.
Due to the frequent occurrence of information leakage incidents, people in various
countries have aroused urgent legislative demands. Governments attach great importance to
the issue of information leakage and a series of policies has been promulgated. For example,
Canada issued a proposed Digital Charter on May 21, 2019, which stipulates that “the content
of personal information shared by individuals shall be controlled, including the subject and
the purpose of the usage of personal information”(Mcmillan.ca, 2019). Chapter III of the EU’s
General Data Protection Regulations, which came into force in May 2018, stipulates that “data
collectors shall provide relevant information when collecting data, and data subjects shall
have the right to access, correct, delete and process data”(Intersoft Consulting, 2018). In
addition to the measures taken by the governments, companies that adopt big data
technology are also aware of the privacy risk because it may undermine the big data
technology adoption process and the company reputation (Baig et al., 2019). They proposed
many differentiated privacy mechanisms to protect users’privacy according to the privacy
preferences of different people (Liang et al., 2020;Winkler and Zeadally, 2016).
Being influenced by the personal information leakage result in the digital world and
privacy protection measures that the governments and companies have taken, users have
complicated subjective views on this issue. There is growing concern that the information
collected by government agencies and corporate organizations may lead to personal
information leakage (Shamsi and Khojaye, 2018). According to a Chinese survey, 79% of
respondents are aware of personal information being leaked and 69.3% of respondents put
forward suggestions to strengthen information security on the Internet, which shows they are
not satisfied with the status of privacy protection (IT, 2017). The users’feelings and behaviors
about information leakage are diverse. These studies agree that people are aware of the
personal information leakage problem and try to avoid it (Yi et al., 2020). Besides, they also
focus on the users’privacy disclosure behaviors (Wang et al., 2016;Esmaeilzadeh, 2020;Deng
and Zhao, 2019). For example, perceived privacy risks can significantly reduce personal
information disclosure intentions, as well as actual information disclosure behaviors (Yu
et al., 2020). People may often be indifferent about the information that does not affect their
interests because of humans’inherent inertia. In sum, people’s views on personal information
leakage and privacy protection are varied and intractable to reveal.
Previous work also pays attention to the methods to protect privacy at the theoretical
(Mousavi et al.,2020;Chang et al.,2018), technical (Chen and Zhao, 2012;Talukder et al., 2010;
Fox et al.,2019) and empirical study (van der Schyff et al.,2020;Zhang et al.,2018) levels.
However, little research has investigated the users’subject pattern about the combination of
informationleakage and privacy protection, which leadsto a gap in this research area.Besides,
citizen sciencehas more flexibilityand diversity than traditionalpolicy research sciencedue to
the public’s increased role (Guerrini et al.,2018).Therefore, this study focuseson mining users’
subjective views onpersonal information leakage and privacy protection, contributing to the
governments’and companies’privacy protection process. Because big data technology has
AJIM
74,5
902
To continue reading
Request your trial