App design based on the protection of personal privacy information in China

Research Article
Open access

App design based on the protection of personal privacy information in China

Xiangru Qin 1*
  • 1 Goldsmiths，University of London    
  • *corresponding author xqin003@campus.goldsmiths.ac.uk
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/14/20230777
ACE Vol.14
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-019-6
ISBN (Online): 978-1-83558-020-2

Abstract

In today's world, digital technology is rapidly evolving and the way people live their lives is changing dramatically. For example, tools such as computers, the internet and mobile phones are bringing us new experiences like never before, while digital technology is also bringing unprecedented advances and innovations to business and science. New opportunities are growing and traditional hierarchies are beginning to fall apart - and with them comes a breakdown in trust. Google, Instagram, Wechat, Xiaohongshu - these disruptive platforms,networks and technologies have changed the current status of their respective industries, making our lives easier while there are bad actors in the unseen grey areas stealing and exploiting our privacy and data. The emergence of COVID-19 in recent years has forced us to use and rely on digital devices with high frequency, especially in China, which has accelerated our transition to a digital world, with both advantages and disadvantages. This article will explore how to better protect the privacy and data of digital nomads within reasonable and lawful limits, and provide an idea as a solution. The article presents the idea of a data protection app called Data Butler, which is an application dedicated to protecting the privacy of its users. The aim is to enable users to enjoy the benefits of big data while protecting their private data. Data Butler is designed to enable users to enjoy the benefits of big data while their private data is protected.

Keywords:

user privacy data, personalised customer, interface app, user profiling, data protection

Qin,X. (2023). App design based on the protection of personal privacy information in China. Applied and Computational Engineering,14,132-144.
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References

[1]. Trusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting. Marketing Science (Providence, R.I.), 35(3), 405-426.

[2]. Anderson, B., Vance, A., Kirwan, C., Jenkins, J., & Eargle, D. (2016). From Warning to Wallpaper: Why the Brain Habituates to Security Warnings and What Can Be Done About It. Journal of Management Information Systems, 33(3), 713-743.

[3]. Gao, Y. (2022). The development of big data in the context of the Law of the People's Republic of China on the Protection of Personal Information[J]. Library Theory and Practice, (4): 4-11.

[4]. Shao M. (2021). Research on the legal protection of personal information in data applications[J]. Legal Expo, (05): 65-66.

[5]. Chen, H. (2018). Revisiting the Privacy Paradox on Social Media With an Extended Privacy Calculus Model: The Effect of Privacy Concerns, Privacy Self-Efficacy, and Social Capital on Privacy Management. The American Behavioral Scientist (Beverly Hills), 62(10), 1392-1412.

[6]. Honerkamp, V. (2020). Predictors of avoidance towards personalization of restaurant smartphone advertising [Summary].

[7]. Park, J. (2014). The effects of personalization on user continuance in social networking sites. Information Processing & Management, 50(3), 462-475.

[8]. ur Rehman, I. (2019). Facebook-Cambridge Analytica data harvesting: What you need to know. Library Philosophy and Practice, 1-11.

[9]. Huang, L. (2021). On the Protection of Privacy in the Internet Era[J]. Business Intelligence, (8): 254

[10]. Kuhn, M. (2018). 147 Million Social Security Numbers for Sale: Developing Data Protection Legislation After Mass Cybersecurity Breaches. Iowa Law Review, 104(1), 417-445.

[11]. Botsman, R. Who can you trust?: how technology brought us together–and why it could drive us apart. Penguin UK, 2017.

[12]. Ding, Z., Yang, R., Cui, P., Zhou, M., & Jiang, C. (2022). Variable Petri Nets for Mobility. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 52(8), 4784-4797.

[13]. Liu, Y. (2023). The dilemma of consent notification rules for personal information processing and suggestions for improvement: interpretation and reflection based on the Law of the People's Republic of China on the Protection of Personal Information[J]. Media, (1): 73-76.

Cite this article

Qin,X. (2023). App design based on the protection of personal privacy information in China. Applied and Computational Engineering,14,132-144.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-019-6(Print) / 978-1-83558-020-2(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.14
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Trusov, M., Ma, L., & Jamal, Z. (2016). Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting. Marketing Science (Providence, R.I.), 35(3), 405-426.

[2]. Anderson, B., Vance, A., Kirwan, C., Jenkins, J., & Eargle, D. (2016). From Warning to Wallpaper: Why the Brain Habituates to Security Warnings and What Can Be Done About It. Journal of Management Information Systems, 33(3), 713-743.

[3]. Gao, Y. (2022). The development of big data in the context of the Law of the People's Republic of China on the Protection of Personal Information[J]. Library Theory and Practice, (4): 4-11.

[4]. Shao M. (2021). Research on the legal protection of personal information in data applications[J]. Legal Expo, (05): 65-66.

[5]. Chen, H. (2018). Revisiting the Privacy Paradox on Social Media With an Extended Privacy Calculus Model: The Effect of Privacy Concerns, Privacy Self-Efficacy, and Social Capital on Privacy Management. The American Behavioral Scientist (Beverly Hills), 62(10), 1392-1412.

[6]. Honerkamp, V. (2020). Predictors of avoidance towards personalization of restaurant smartphone advertising [Summary].

[7]. Park, J. (2014). The effects of personalization on user continuance in social networking sites. Information Processing & Management, 50(3), 462-475.

[8]. ur Rehman, I. (2019). Facebook-Cambridge Analytica data harvesting: What you need to know. Library Philosophy and Practice, 1-11.

[9]. Huang, L. (2021). On the Protection of Privacy in the Internet Era[J]. Business Intelligence, (8): 254

[10]. Kuhn, M. (2018). 147 Million Social Security Numbers for Sale: Developing Data Protection Legislation After Mass Cybersecurity Breaches. Iowa Law Review, 104(1), 417-445.

[11]. Botsman, R. Who can you trust?: how technology brought us together–and why it could drive us apart. Penguin UK, 2017.

[12]. Ding, Z., Yang, R., Cui, P., Zhou, M., & Jiang, C. (2022). Variable Petri Nets for Mobility. IEEE Transactions on Systems, Man, and Cybernetics. Systems, 52(8), 4784-4797.

[13]. Liu, Y. (2023). The dilemma of consent notification rules for personal information processing and suggestions for improvement: interpretation and reflection based on the Law of the People's Republic of China on the Protection of Personal Information[J]. Media, (1): 73-76.

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