Abstract
In recent years, with the advent of the data age, the collection, release, and analysis of massive data have become more convenient, and information sharing has become more common. At the same time, there is also the threat of privacy information. How to effectively solve the potential privacy issues in the process of data release is our research direction. Anonymity technology is currently the main technology used in privacy protection. The main work of this article is to design a personal data collection and privacy legal protection platform, analyzes the reasons for privacy leakage, and establishes an M-diversity anonymous model platform suitable for the protection of personal privacy information and data. In-depth research has been conducted on common protection techniques. In addition, the performance test of the designed privacy protection platform system is carried out, and the number of the privacy protection data platform system functions leaking the privacy of the victims is analyzed. The experimental results show that the M-diversity anonymous model is suitable for the protection of personal privacy information and data and can enhance the information and data suitable for personal privacy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghibellini N (2018) Some aspects of the EU’s new framework for personal data privacy protection. The Business Lawyer 73(1):207–213
Mishra N (2018) Personal data protection and data localization: rethinking your enterprise multi-cloud strategy. Dataquest 36(9):12–14
Gao YL, Chen XB, Xu G et al (2020) A new blockchain-based personal privacy protection scheme. Multimedia Tools Appl 4:1–14
Sun AS (2017) Research on protection of consumer personal information from the perspective of consumer protection law. Appl Sci Manage Res 4(1):108–120
Polykalas SE, Prezerakos GN (2019) When the mobile app is free, the product is your personal data. Info 21(2):89–101
Zharova AK, Elin VM (2017) The use of big data: a Russian perspective of personal data security. Comput Law Secur Rep 33(4):482–501
Chua HN, Herbland A, Wong SF et al (2017) Compliance to personal data protection principles: a study of how organizations frame privacy policy notices. Telematics Inform 34(4):157–170
Stitilis D, Laurinaitis M (2017) Treatment of biometrically processed personal data: problem of uniform practice under EU personal data protection law. Comput Law Secur Rep 33(5):618–628
Oastler K (2018) Spot the difference: the change in the definition of ‘personal data’ in the UK. Privacy & Data Protection 19(1):12–15
Chua HN, Wong SF, Chang Y et al (2017) Unveiling the coverage patterns of newspapers on the personal data protection act. Gov Inf Q 34(2):296–306
Voss WG (2017) european union data privacy law reform: general data protection regulation, privacy shield, and the right to delisting. Business Lawyer 72(1):221–233
Corbet R (2018) GDPR series: personal liability for DPOs. Privacy & Data Protection 18(5):14–15
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Che, L. (2023). Privacy Information Protection System Based on Data Collection Algorithm. In: Jansen, B.J., Zhou, Q., Ye, J. (eds) Proceedings of the 2nd International Conference on Cognitive Based Information Processing and Applications (CIPA 2022). CIPA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 156. Springer, Singapore. https://doi.org/10.1007/978-981-19-9376-3_20
Download citation
DOI: https://doi.org/10.1007/978-981-19-9376-3_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9375-6
Online ISBN: 978-981-19-9376-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)