IJBTT

Securing Data Platforms: Strategic Masking Techniques for Privacy and Security for B2B Enterprise Data
	© 2023 by IJCTT Journal
	Volume-71 Issue-11
	Year of Publication : 2023
	Authors : Mandar Khoje
	DOI :  10.14445/22312803/IJCTT-V71I11P107

How to Cite?

Mandar Khoje, "Securing Data Platforms: Strategic Masking Techniques for Privacy and Security for B2B Enterprise Data," International Journal of Computer Trends and Technology, vol. 71, no. 11, pp. 46-54, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I11P107

Abstract
In today’s digital age, the imperative to protect data privacy and security is a paramount concern, especially for business-to-business (B2B) enterprises that handle sensitive information. These enterprises are increasingly constructing data platforms, which are integrated suites of technology solutions architected for the efficient management, processing, storage, and data analysis. It has become critical to design these data platforms with mechanisms that inherently support data privacy and security, particularly as they encounter the added complexity of safeguarding unstructured data types such as log files and text documents. Within this context, data masking stands out as a vital feature of data platform architecture. It proactively conceals sensitive elements, ensuring data privacy while preserving the information’s value for business operations and analytics. This protective measure entails a strategic two-fold process: firstly, accurately pinpointing the sensitive data that necessitates concealment, and secondly, applying sophisticated methods to disguise that data effectively within the data platform infrastructure. This research delves into the nuances of embedding advanced data masking techniques within the very fabric of data platforms and an in-depth exploration of how enterprises can adopt a comprehensive approach toward effective data masking implementation by exploring different identification and anonymization techniques.

Keywords
Data masking, Data platform, Privacy, PII detection.

Reference

[1] Art. 4 GDPR–Definitions, InterSoft Consulting. [Online]. Available: https://gdpr-info.eu/art-4-gdpr/
[2] G.K. Ravi Kumar, B. Justus Rabi, and T.N. Manjunath, “A Study on Dynamic Data Masking with its Trends and Implications,” International Journal of Computer Applications, vol. 38, no. 6, pp. 19-24, 2012.
[Google Scholar] [Publisher Link]
[3] Data Masking, Wikipedia. [Online]. Available: https://en.wikipedia.org/w/index.php? title=Data_masking\&oldid=1182980899
[4] Regular expression, Wikipedia. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Regular_expression\&oldid=1183698658
[5] Simon Heron, “Advanced Encryption Standard (AES),” Network Security, vol. 2009, no. 12, pp. 8-12, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Yu-Chih Wei, Tzu-Yin Liao, and Wei-Chen Wu, “Using Machine Learning to Detect PII from Attributes and Supporting Activities of Information Assets,” The Journal of Supercomputing, vol. 78, pp. 9392-9413, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Chatgpt, Chat.OpenAi. [Online]. Available: https://chat.openai.com
[8] Symmetric-Key and Algorithm, Wikipedia. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Symmetric-key_algorithm\&oldid=1183120665
[9] Public-key Cryptography, Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/index.%20php?oldid=1184320387&title=Publickey_cryptography\
[10] Hash function, Wikipedia. [Online]. Available: https://en.wikipedia.org/w/index.php? title=Hash_function\&oldid=1184500231
[11] Salt, Wikipedia. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Salt_