Search results
Results from the WOW.Com Content Network
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.
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 ]
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.
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 ]
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]
DataMask protects you by disguising your every keystroke. Ward off attackers with patented keystroke protection safeguarding your personal information.
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.
The general term for this problem is data remanence. In some contexts (notably the US NSA, DoD, and related organizations), "sanitization" typically refers to countering the data remanence problem. However, the retention may be a deliberate feature, in the form of an undo buffer, revision history, "trash can", backups, or the like.