> For the complete documentation index, see [llms.txt](https://docs.fractalworks.io/joule/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fractalworks.io/joule/components/processors/transformation/obfuscation/bucketing.md).

# Bucketing

## Objective

The objective of this page is to explain how **date** and **number** values in a `StreamEvent` can be obfuscated using variance tolerances to protect sensitive information, such as date of birth, salary and age.

This method, similar to blurring, adjusts values within a specified range while preserving the original distribution and accuracy, ensuring privacy without compromising data utility.

{% hint style="info" %}
This is more akin to blurring rather than obfuscation
{% endhint %}

## Date variance

Each date value for a specified field will be varied by a **random number of days**, whilst maintaining the original variance, range and distribution.

This can useful where it would otherwise be possible to identify individuals by an exact match, such as date of birth.

### Example

This code defines an **obfuscation** strategy named `dateBucketing` applied to the `dateOfBirth` field.

It uses **date bucketing** with a **variance of 30**, meaning that the actual date of birth will be obscured by randomly shifting the date within a 30-day range.

This protects the exact date while maintaining some level of accuracy.

```yaml
obfuscation:
  name: dateBucketing
  fields:
    dateOfBirth:
      date bucketing:
        variance: 30
```

### Attributes schema

<table><thead><tr><th width="193">Attribute</th><th width="217">Description</th><th width="219">Data Type</th><th data-type="checkbox">Required</th></tr></thead><tbody><tr><td>variance</td><td>Maximum number of days to vary the source date</td><td><p>Integer</p><p>Default: 120</p></td><td>false</td></tr></tbody></table>

## Number variance

Each number can be varied by a **random percentage**, whilst maintaining the original variance, range and distribution.

This can useful where it would otherwise be possible to identify individuals salary by an exact match.

### Example

This code defines an **obfuscation** strategy called `numberBucketing` applied to two fields: `salary` and `age`.

1. <mark style="color:green;">**salary**</mark>\
   The value of `salary` will be obscured by a **variance of 0.25**, meaning the value can fluctuate by 25% up or down.
2. <mark style="color:green;">**age**</mark>\
   The value of `age` will be obscured by a **variance of 0.10**, meaning the age can fluctuate by 10% up or down.

This technique hides the exact values while maintaining general data accuracy within the specified variance.

```yaml
obfuscation:
  name: numberBucketing
  fields:
    salary:
      number bucketing:
        variance: 0.25
    age: 
      number bucketing:
        variance: 0.10
```

### Attributes schema

<table><thead><tr><th width="193">Attribute</th><th width="217">Description</th><th width="219">Data Type</th><th data-type="checkbox">Required</th></tr></thead><tbody><tr><td>variance</td><td>Variance multiplier to be applied to random masking process</td><td><p>Double</p><p>Default: 0.15</p></td><td>false</td></tr></tbody></table>