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Generalization and perturbation are the two popular anonymization approaches for relational data. [4] The process of obscuring data with the ability to re-identify it later is also called pseudonymization and is one way companies can store data in a way that is HIPAA compliant.
Such data has proved to be very valuable for researchers, particularly in health care. GDPR-compliant pseudonymization seeks to reduce the risk of re-identification through the use of separately kept "additional information". The approach is based on an expert evaluation of a dataset to designate some identifiers as "direct" and some as "indirect."
The pseudonym allows tracking back of data to its origins, which distinguishes pseudonymization from anonymization, [9] where all person-related data that could allow backtracking has been purged. Pseudonymization is an issue in, for example, patient-related data that has to be passed on securely between clinical centers.
Anonymization refers to irreversibly severing a data set from the identity of the data contributor in a study to prevent any future re-identification, even by the study organizers under any condition. [10] [11] De-identification may also include preserving identifying information which can only be re-linked by a trusted party in certain situations.
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.
Because k-anonymization does not include any randomization, attackers can make reliable, unambiguous inferences about data sets that may harm individuals. For example, if the 19-year-old John from Kerala is known to be in the database above, then it can be reliably said that he has either cancer, a heart-related disease, or a viral infection.
Data on students' success in college, including whether they enrolled in remedial courses; Data on whether K-12 students are prepared to succeed in college; A system of auditing data for quality, validity, and reliability; The ability to share data from preschool through post-secondary education data systems.
In 1998 Sweeney published a now famous example about data de-anonymization, demonstrating that a medical dataset that was in the public domain, can be used to identify individuals, regardless the removal of all explicit identifiers, when the medical dataset was combined with a public voter list.