Securing Data Platforms: Strategic Masking Techniques for Privacy and
Security for B2B Enterprise Data
- URL: http://arxiv.org/abs/2312.03293v1
- Date: Wed, 6 Dec 2023 05:04:37 GMT
- Title: Securing Data Platforms: Strategic Masking Techniques for Privacy and
Security for B2B Enterprise Data
- Authors: Mandar Khoje
- Abstract summary: Business-to-business (B2B) enterprises are increasingly constructing data platforms.
It has become critical to design these data platforms with mechanisms that inherently support data privacy and security.
Data masking stands out as a vital feature of data platform architecture.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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