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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.
De-anonymization is the reverse process in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source. [3] Generalization and perturbation are the two popular anonymization approaches for relational data. [ 4 ]
Common strategies of de-identification are masking personal identifiers and generalizing quasi-identifiers. Pseudonymization is the main technique used to mask personal identifiers from data records, and k-anonymization is usually adopted for generalizing quasi-identifiers.
These data are released after applying some anonymization techniques like removing personally identifiable information (PII) such as names, addresses and social security numbers to ensure the sources' privacy. This assurance of privacy allows the government to legally share limited data sets with third parties without requiring written permission.
Pseudonymized data can be restored to its original state with the addition of information which allows individuals to be re-identified. In contrast, anonymization is intended to prevent re-identification of individuals within the dataset.
The SPARK Matrix report stresses data masking as “a pivotal tool” that enables organizations to “significantly diminish vulnerabilities to unauthorized access and potential data breaches.” Mage Data’s advanced masking methodologies are based on a sophisticated data discovery process and range from data element scrambling to tokenization.
Data masking of structured data is the process of obscuring (masking) specific data within a database table or cell to ensure that data security is maintained and sensitive information is not exposed to unauthorized personnel. [7]
In June, his division launched a data tool that analyzes $4.8 trillion worth of deals across 6,500 funds. This database can be used in a slew of ways, from backing up valuations in negotiations to ...
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