The greatest concern from the corporate management perspective is about how easy one can work without the fear of losing sensitive data to unknown users. Take, for example, the rise of insecurity in the cryptocurrency world has attracted intense anxiety for the future of data.
More than often, wallets have been accessed by unauthorized parties hence causing fear among the investor. Data masking has come as the right solution to possible challenges. Beat your concern with these data masking techniques!
What is the most Common Data Masking Techniques?
There are a variety of techniques used by potential IT professionals while masking data. At datasunrise.com, data security is given the priority. We guarantee you the best data safety and 24/7 support for your experience.
We have unlimited approaches to data safety. Is the protection of your financial and personal data becoming a major concern? Here are the top-rated techniques of data masking and how you can apply to your business.
1. Data Encryption
This is where a key is set up to grant access to the data by any authorized users. So unacceptable person will not have access to the data once it is encrypted. It is one of the most secure and complex categories of data masking. Data is masked by a strong encryption algorithm.
2. Character Scrambling
This is the basic technique of data masking that involves character scrambling, where characters are jumbled randomly, so it hides the original content.
3. Number and Date Variance
Once it’s properly applied, number and date variance provides a helpful set of data by restricting essential financial information or transaction dates. Ensure accuracy by using the same variance to all values in a set, so, the range doesn’t change.
The numeric variance approach is useful to financial as well as data-driven information. This method leaves a meaningful set of values in the commercial range of data such as payroll.
On the other hand, date information uses this technique. If the entire data set should retain demographic as well as actuarial data integrity, applying a significant random numeric variance preserves the date distribution, although it still limits back traceability to known entities based on known dates and birth value of the records masked.
4. Substitution
It effectively mimics the feel and looks of actual data by not compromising one’s personal data. So, the real value is substituted by inauthentic value. This then protects actual data from breach threats. Thus, substitution is the widely employed methods of data masking, and, it preserves the authentic look of the data records.
That allows masking to occur in a way that authentic look is substituted for the existing value. Several fields of this approach offer excellent benefits. So, if you are dealing with the right source of data, which involves records of customers, then the real-life surname can be substituted randomly from a customized look up the file.
Ideally, substitution should be fairly extensive, so, with heavy substitution datasets and application of customized data substitution, sets need to be a key element to evaluate data masking.
In Conclusion, the digital revolution has brought the need for data security. It is essential that you make the best decision about the right technique for your data security. Discover the best approaches to data masking today!
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