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Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Data masking or data obfuscation is the process of modifying sensitive data in such a way that it is of no or little value to unauthorized intruders while still being usable by software or authorized personnel. Data masking can also be referred as anonymization, or tokenization, depending on different context.
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...
All data is tainted until proven otherwise - means that all data must be handled in a way that does not expose the rest of the runtime environment without verifying integrity. All code is insecure until proven otherwise - while a slight misnomer, does a good job reminding us to never assume our code is secure as bugs or undefined behavior may ...
As a security best practice, [47] independent assessment and validation of any technologies used for data protection, including tokenization, must be in place to establish the security and strength of the method and implementation before any claims of privacy compliance, regulatory compliance, and data security can be made. This validation is ...
Software-based security solutions encrypt the data to protect it from theft. However, a malicious program or a hacker could corrupt the data to make it unrecoverable, making the system unusable. Hardware-based security solutions prevent read and write access to data, which provides very strong protection against tampering and unauthorized access.
Pinterest CEO: To protect our kids online, Congress must make digital IDs the national standard—and require OS makers to share age-validation data with apps Bill Ready September 23, 2024 at 1:57 AM
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
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