Data Masking Statistics


Steve Goldstein
Steve Goldstein
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Steve Goldstein runs LLCBuddy, helping entrepreneurs set up their LLCs easily. He offers clear guides, articles, and FAQs to simplify the process. His team keeps everything accurate and current, focusing on state rules, registered agents, and compliance. Steve’s passion for helping businesses grow makes LLCBuddy a go-to resource for starting and managing an LLC.

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Data Masking Statistics 2023: Facts about Data Masking outlines the context of what’s happening in the tech world.

LLCBuddy editorial team did hours of research, collected all important statistics on Data Masking, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

Are you planning to form an LLC? Maybe for educational purposes, business research, or personal curiosity, whatever the reason is – it’s always a good idea to gather more information about tech topics like this.

How much of an impact will Data Masking Statistics have on your day-to-day? or the day-to-day of your LLC Business? How much does it matter directly or indirectly? You should get answers to all your questions here.

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On this page, you’ll learn about the following:

Top Data Masking Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 12 Data Masking Statistics on this page 🙂

Data Masking “Latest” Statistics

  • In the U.S., 75% of Americans would prefer to interact with their specialists via email.[1]
  • 60% of Americans would feel like checking their medical reports digitally or electronically.[1]
  • According to the U.S. General Accounting Office, 61% of the data mining projects run by federal bureaus utilized personal information.[1]
  • 67% of the data mining projects in the U.S. are from the private sector concerning personal information.[1]
  • 93% believed organizations distributing personal data for trivial use should be obliged to gain approval from the individuals.[1]
  • 82% of networked users have declined to provide personal information.[1]
  • 34% of Americans have lied when inquired about their personal quirks and inclinations.[1]
  • Manhattan Research Report discovered that only 2.5 million or fewer than 1% of the population had ever employed a PHR.[1]
  • 87% of Americans can be uniquely re-ascertained using deterministic performance linkage with three non-perceptive attributes: gender, birth, and 5-digit zip code, which are reachable from voter registration transcripts feasible to the electorate.[1]
  • Over 70 million in the U.S. have some portion of their medical history in computerized format, and analysts predict EMRs to keep over $81 billion per year.[1]
  • The MSPE rate using the masked data is only 1.39% bigger than that of the unconventional data.[1]
  • For the slightly masked data, they found zero RBID and a 1.05% rise in MSPE, with a correlation count of merely 25 from 126,358 records.[1]

Also Read

How Useful is Data Masking

One of the primary benefits of data masking is that it helps organizations comply with regulations and standards related to data privacy and security. By masking sensitive information such as personally identifiable information (PII) or financial data, organizations can limit the risk of a data breach and the potential consequences of non-compliance with regulations like GDPR or HIPAA. Masking data ensures that even if unauthorized users gain access to a database or system, they will only see fictitious or obscured information, protecting the privacy of individuals and the security of the organization’s data.

Data masking also plays a crucial role in data testing and development environments. In these settings, developers and testers often need access to realistic data to assess the functionality of applications and systems. However, using real data poses privacy risks and could violate regulations if proper precautions are not taken. Data masking allows organizations to create realistic test environments without compromising the security of sensitive information, striking a balance between privacy and functionality.

Moreover, data masking can be a cost-effective solution for organizations that want to minimize the risk of a data breach without requiring large-scale changes to their infrastructure. Implementing robust encryption or access controls can be complex and costly, whereas data masking is a relatively straightforward and affordable way to protect sensitive information. By obscuring or replacing identifying data, organizations can add an extra layer of security to their systems without extensive investment or disruption.

Despite these advantages, the utility of data masking may vary based on the specific needs and circumstances of an organization. For some organizations, data masking may not provide sufficient protection against advanced threats or may be difficult to implement in complex data environments. In these cases, organizations may need to supplement data masking with other security measures to effectively safeguard their data.

Furthermore, while data masking can make sensitive information unreadable to unauthorized users, it is essential to remember that it is not a foolproof solution. Determined attackers may still find ways to de-identify masked data or uncover vulnerabilities in the masking process. Organizations should regularly assess and improve their data masking techniques to ensure that they are robust against emerging threats and compliance requirements.

In conclusion, data masking is a valuable tool for organizations looking to protect sensitive information, comply with regulations, and enhance the security of their data environments. However, the effectiveness of data masking depends on a variety of factors, including the organization’s specific needs, the complexity of its data ecosystem, and the evolving threat landscape. By understanding the strengths and limitations of data masking, organizations can make informed decisions about how to best protect their data and sensitive information.

Reference

  1. nih – https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3839961/

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