Data anonymization is the process of protecting private or individual sensitive information by either erasing or encrypting the personal identifiers that form the connection between an individual and stored data. This helps in retaining the data while keeping the source anonymous.
What Is the Need For Anonymization Of Patient Data?
- Data science including collection and analysis of patient data is of immense importance for improving healthcare. It forms the basis of healthcare research for improving drug discovery, predicting epidemics, designing advanced cures, etc.
However, the law requires healthcare researchers to keep the PHI (Personal Health Information) of people secure. So, the only way of using patient’s data for research is to get their consent beforehand. This places a limitation on the data sets as some patients may decline the consent. Data anonymization lifts certain restrictions as it removes the patient’s identifiers and renders the data anonymous. It provides healthcare researchers the ability to access extensive, coherent, and historic data that can be built upon without damaging patient trust.
- Second reason that emphasizes the importance of genuine anonymization of patient data is that patients may be reluctant to seek medical attention if they fear that their PHI may be shared with someone. Genuine anonymization helps the healthcare institutes in offering privacy assurance to their patients.
- An information leak or disclosure that an individual has tested positive for STIs such as HIV/AIDS can invite discrimination or social stigma. Anonymization of such data helps in reducing the risk of such disclosure and maintaining the privacy and confidentiality of patient data.
- Another reason for incorporating genuine anonymization of patient data in the healthcare industry is to keep the data secure from cyber criminals who may cause a data breach and negatively affect the patients.
What Data Anonymization Techniques Can Be Used?
Data Masking: Real data is hidden by altering values. For example, a mirror of a dataset may be created and the value characters may be replaced with symbols such as ‘*’ or ‘x’.
Pseudonymization: The private identifiers such as name, address, etc. are replaced with face identifiers or pseudonyms.
Generalization: Some of the identifier data is removed while retaining a measure of data accuracy. For example, removing house number from the patient’s address while retaining the road name.
Data Swapping: It is also known as shuffling or permutation. The dataset attribute values are rearranged so that they don’t correspond with original values.
Data Perturbation: The original data set is modified by adding noise to the data and rounding off the numbers such as age or house number of the patient.
Synthetic Data: An artificial data set is created instead of altering the original dataset based on patterns and statistical analysis.
For more information on the importance of genuine anonymization of patient data and methods of implementation in healthcare, call Centex Technologies at (972) 375 - 9654.