Data Anonymisation Techniques

Thinking back to when I first began working on projects where I was solely responsible for data security & customer privacy, I experienced so much personal growth as I learned how to build relationships with development teams and project managers and improve my security consultation skills by adding requirements that mitigate both security and privacy risks early in the development lifecycle to ensure they are baked in and not retrofitted while also minimising delays to timelines.

I remember a smaller project from a non-tech team who wanted to improve a business process that had an enormously tedious overhead to anonymise a dataset. Thankfully, they engaged with security very early to make sure they were not breaking any rules and wanted full transparency and awareness that they were only doing this if everyone was happy. I love these kinds of stakeholders!

To solve this problem, I needed to present options to the team for the techniques that would protect the privacy of the individuals in the dataset but still allow them to extract value from the data. Let's run through the various techniques.

What is data anonymisation?

Data anonymisation is the process of removing or altering Personally Identifiable Information (PII) in data so that the individuals represented in the data cannot be identified. This is often done to protect the privacy of individuals when their data is used for research or other purposes. It's also a very useful security control to have in place if a data breach is suffered as the data is difficult to attribute to individuals. Difficult, not impossible!

Data Anonymisation Techniques

Data masking: Data masking involves replacing sensitive data with randomised or fictitious values, such as replacing a person's name with a pseudonym or replacing their address with a fake address. This technique is often used to protect the privacy of individuals when their data is used for research or other purposes, as it makes it difficult to identify specific individuals.

Data perturbation: This involves adding noise or random errors to the data to make it difficult to identify specific individuals. This could include adding random errors to numerical data or replacing specific words with similar-sounding words.

Data aggregation: Data aggregation involves grouping data together and only releasing aggregated statistics, rather than individual records. For example, instead of releasing data on the exact number of people in a particular age group, data aggregators might release data on the number of people in broad age ranges, such as 18-24, 25-34, etc.

Data suppression: Data suppression involves removing or suppressing data that could potentially be used to identify individuals. This could include removing specific data fields or limiting the number of records that are released.

Data generalisation: Data generalisation involves replacing specific values in the data with more general categories. For example, instead of releasing a person's exact age, it's replaced with their age range, such as 18-29, 30-39, 40-49 etc. This technique can help to protect the privacy of individuals by making it more difficult to identify them.

A Word of Caution

It's important to note that data anonymisation is not a foolproof method for protecting the privacy of individuals, as there is always the possibility that someone could re-identify individuals in the data by combining it with other sources of information. Therefore, it's vital to carefully evaluate the potential risks and benefits of using anonymised data, and to use appropriate safeguards to protect the privacy of individuals.

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