Data Binning
Data Binning is a technique which can preserve the granularity of data — keeping individual-level records — whilst reducing the ability to readily identify individuals. This recognises that certain information like ages and town of birth can be used as quasi-identifiers. Implementing data-binning replaces a specific field such as age with a reference to a 'bucket' or 'bin', such as age range. This can reduce the risk of using this information for identifying individuals.
Typically, the technique is focussed on reducing the probability of a risk occurring - assuming the risk is associated with reidentification. The impact of the risk if it occurs is unaffected.
When might Data Binning be valuable?
This is a useful technique if the value of the dataset resides in the ablility to analyse the features of individual entries, rather than descriptive statitics that might be released in a [summarised or aggregated]({{ "/technique/aggregation" | relative_url }}) data set. If the individual fields are otherwise too risky to release, comprising personal or other sensitive data, a binned version can provide a means of reducing the risk of identifying an individual.
Key considerations and risks
Data binning is a relatively simple and understandable technique that retains much of the value in the raw data. The following considerations will allow a safe release.
Select an appropriate data bin
Examples of data binning methods might include:
| Specific data | Data Bin | Example |
|---|---|---|
| Age | Age Range | 15-19, 20-24 |
| Date of birth | Year or decade of birth | 1950s, 1960s |
| Full postcode | Postcode outward code | HX7 |
| Full postcode | Administrative region | Calderdale |
| Full postcode | Census geography | MSOA |
Be careful when selecting data bins that related datasets might have different bins, rendering it tricky to compare between datasets. ONS data, for example, uses 5-year age bands (0-4, 5-9, etc). Selecting incompatible age binning (16-21, 22-26, etc) will make the datasets less valuable.
Residual reidentification risk
The threat of reidentification is significant where there are small populations that fit a given categorisation. As an example people living in a sparsely populated rural geographic area (e.g. postcode) might still be be identifiable if there are a small number of people in a given age band. If there are other identifying characteristics, it can be possible to identify individuals in the dataset. Under these circumstances, redaction or aggregation might also be required.