Data de-identification, a subset of dynamic data masking, disassociates data from the original person to whom it was tied. Data de-identification makes it possible to reuse and share information with third parties by removing or altering personal identifiers. This necessitates the elimination or alteration of all references to an individual.
Most people think of medical data when they hear “data de-identification” because HIPAA explicitly mandates the procedure. HIPAA specifies two distinct approaches to data anonymization: the Safe Harbor and the Expert Determination.
How Data De-identification is done
Achieving automated tagging requires ensuring that data classifiers accurately reflect the contents of data sources. To de-identify data, direct and indirect identifiers must be categorized and labeled. Direct identifiers, such as a Social Security number, passport number, or tax ID, can be directly linked to a specific person. The remaining IDs are classified as “indirect identifiers,” and they often consist of generic characteristics of the individual. Because of this, data teams can de-identify information with far more ease.
The presentation and context of the data are affected by the technological and organizational safeguards used, which include restrictions on who may access the data and for what purposes. Data engineering and operations teams often use pseudonymization as a first step in the de-identification process. Then, the data may be rendered anonymous by using a mix of dynamic data masking techniques and data access rules.
A user’s privacy is protected without their knowledge or involvement in the de-identification process. When entering new information into the system, users may choose which fields to make public and which to keep secret. Using the Create Document endpoint, individuals may transmit the identifying information to TrueVault. In the same way that users do not have to be aware of de-identifying their data, they also need not be aware of the re-identification procedure.
1. Generalizing (k-anonymization)
k-anonymization is a method for protecting against re-identification by concealing individuals within groups and suppressing indirect identifiers for groups with a size less than a threshold value, k. This prevents assaults based on assuming someone else’s identity or establishing a connection between two people. The value of data sets may be increased without sacrificing privacy with the aid of this de-identification method. K-anonymization can extend the data’s scope further once the main identifiers have been hidden.
K-anonymization is most effective with attribute-based access control, real-time data use monitoring, and randomization to shield sensitive attributes.
2. Randomizing (differential privacy and randomized response)
Once direct identities have been concealed, a randomization approach known as differential privacy can be implemented. Differential privacy can be handled in either a local or a global fashion.
- Local differential privacy – As a form of data randomization, local differential privacy provides a mathematical guarantee against attribute-based inference attacks and is thus often applied to sensitive attributes. Since accumulating too much information on a given record might weaken privacy, attribute values are randomized to reduce the amount of personal information an attacker can deduce while still retaining some analytic benefit. To that end, anyone whose information is part of the searched data set can dispute certain details about their profiles.
- Global differential privacy: Randomizing large amounts of data is the basis of global differential privacy. People whose information is part of the data collection being searched might claim they aren’t part of it. This method provides a mathematical assurance against identity-, attribute-, participation-, and relation-based inference attacks while restricting data consumers to formulating only aggregate queries like count, average, max, min, etc.
Global differential privacy allows for the computation of aggregate statistics in a privacy-preserving manner. In contrast, local differential privacy may be achieved for individual columns requiring a high level of security with a randomized response.
Application in Healthcare
De-identified data has emerged as a useful resource for researchers and healthcare professionals. By eliminating personally identifiable information from shared data, medical researchers may better collaborate to develop new diagnostics, therapies, and preventative measures. In violation of HIPAA, the de-identification procedure allows for the compliant transfer of information across businesses.
Patient data can be shared without risking a violation of HIPAA regulations if it has been de-identified first. Direct identifiers can include a patient’s name, address, medical record information, etc. Gender, ethnicity, age, etc., are all examples of indirect identifiers. Patient privacy requires that direct identification be scrubbed from the data, but indirect identifiers can be left in place so that researchers can still analyze aggregated data.
Benefits of De-identification
- By removing personal identifiers, data becomes more usable and may be safely licensed or shared with third parties.
- There may be no need to notify data breaches or leaks if the information is no longer personally identifiable. Protecting against potential dangers can reduce exposure.
- De-identifying patient data allows healthcare professionals to share information with other groups for medical research and patient care. A further benefit of de-identification is reduced exposure to potential HIPAA breaches.
- Large data analysis systems can work together if personal information is removed.
To sum it up
Data de-identification is when your system removes all traces of a person’s identity from their Protected Health Information (PHI). It’s the simplest approach to conformity that doesn’t limit your technological adaptability. De-identified data can be stored anywhere, and the infrastructure and programming that interacts with it do not need to be HIPAA compliant.
Medical researchers have benefited much from de-identifying patient data, and this work has led to important discoveries that have helped advance patient care. Health researchers and practitioners can benefit from using de-identified data. After privacy concerns are addressed, the data can contribute to healthcare advancements. De-identified data allows healthcare practitioners to safely and securely communicate patient information for research purposes while still protecting patients’ privacy and being in compliance with HIPAA regulations.
Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.