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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.
When applied to metadata or general data about identification, the process is also known as data anonymization. Common strategies include deleting or masking personal identifiers, such as personal name, and suppressing or generalizing quasi-identifiers, such as date of birth.
Location data - series of geographical positions in time that describe a person's whereabouts and movements - is a class of personal data that is specifically hard to keep anonymous. Location shows recurring visits to frequently attended places of everyday life such as home, workplace, shopping, healthcare or specific spare-time patterns. [ 14 ]
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 ]
Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata. The purpose of SDC is to protect ...
DataMask protects you by disguising your every keystroke. Ward off attackers with patented keystroke protection safeguarding your personal information.
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]
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.