How can you de-identify data in databases?

3 How to choose a de-identification method?

The choice of a de-identification method depends on several factors, such as the type, structure, and sensitivity of the data, the level of de-identification required, the intended use and audience of the data, and the available resources and tools. Common de-identification methods include aggregation, which involves grouping or summarizing data into larger categories or averages; generalization, which involves replacing specific or detailed values with broader or less precise ones; masking, which involves hiding or obscuring part or all of the data values; encryption, which involves transforming the data values into a coded form that can only be decrypted with a key or a password; and hashing, which involves converting data values into a fixed-length string of characters that can only be matched with the original values with a function or an algorithm. All these methods reduce the granularity and variability of the data, its uniqueness and specificity, its readability and recognizability, its accessibility and intelligibility, as well as its traceability and reversibility.

4 How to evaluate the effectiveness of de-identification?

The effectiveness of de-identification is measured by how well it balances the privacy and utility of the data. To ensure a good de-identification method, one must minimize the risk of re-identification, which is the ability to link the de-identified data back to the data subjects. To evaluate the effectiveness of de-identification, risk assessment must be conducted to identify and analyze potential sources, scenarios, and consequences of re-identification. Utility assessment is another important factor, as it evaluates and compares the quality, accuracy, and usefulness of the de-identified data for its intended use and audience. Lastly, testing and validation should be performed to verify the de-identification process and results. This includes selecting and implementing a de-identification method, documenting and communicating parameters and assumptions, and verifying and correcting any errors or anomalies.

5 How to de-identify data in databases?

De-identifying data in databases requires a combination of technical and organizational skills and practices. To ensure successful de-identification, you need to define the scope and objectives of the project, such as the data sources, elements, level, and use. Additionally, you should review and comply with the relevant ethical and legal standards and guidelines. Selecting and applying the appropriate de-identification method and tools is also essential. To evaluate and monitor the effectiveness of the de-identification process, you need to conduct risk assessment, utility assessment, testing, and validation. Lastly, it’s important to document and communicate the process and results to stakeholders.