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.
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.
This disguises your data so cyber crooks and threatening websites can’t even read it. Anti-phishing – DataMask by AOL proactively diverts you away from phishing sites (websites designed to steal your personal information) so you won't be tricked into giving away your usernames and passwords.
To open DataMask, double-click the DataMask icon on your Windows system tray or click the Scrambler at the top of your web browser.
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 ]
DataMask protects you by disguising your every keystroke. Ward off attackers with patented keystroke protection safeguarding your personal information.
Data Secure by AOL is an all-in-one plan that includes 4 industry-leading products that help secure your sensitive data from online threats and data breaches. Get started today! Keep intruders out: McAfee Multi Access works around the clock to help keep hackers out and continually searches for viruses and malware that are trying to breach your ...
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]