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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.
In Electronic Health Records (EHR's) data masking, or controlled access, [1] is the process of concealing patient health data from certain healthcare providers. Patients have the right to request the masking of their personal information, making it inaccessible to any physician, or a particular physician, unless a specific reason is provided. [2]
A next-generation firewall (NGFW) or intrusion detection system (IDS) are common examples of technology that can be leveraged to perform DLP capabilities on the network. [7] [8] Network DLP capabilities can usually be undermined by a sophisticated threat actor through the use of data masking techniques such as encryption or compression. [9]
e.g. When entering into a text box a phone number on a data capture form, in the format "(111) 111 1111" the area code brackets, the space between the number and the area code will automatically be placed in. Generally speaking, an input mask is a user-generated set of rules; e.g., a maximum of 45 characters.
Data sanitization policy must be comprehensive and include data levels and correlating sanitization methods. Any data sanitization policy created must be comprehensive and include all forms of media to include soft- and hard-copy data. Categories of data should also be defined so that appropriate sanitization levels will be defined under a ...
Tokenization, when applied to data security, is the process of substituting a sensitive data element with a non-sensitive equivalent, referred to as a token, that has no intrinsic or exploitable meaning or value. The token is a reference (i.e. identifier) that maps back to the sensitive data through a tokenization system.
This may include masking the data from users (for example so banking customer representatives can only see the last four digits of a customer's national identity number), developers (who need real production data to test new software releases but should not be able to see sensitive financial data), outsourcing vendors, etc. [8]
A famous example is the AOL search data scandal. The AOL example of unauthorized re-identification did not require access to separately kept “additional information” that was under the control of the data controller as is now required for GDPR compliant Pseudonymisation, outlined below under the section "New Definition for Pseudonymization ...