For the first time in its 200-year-old history, the US Census Bureau has announced that this year’s survey will implement new standards to safeguard citizen data. The government body is implementing differential privacy for this.

Differential privacy as a concept has been around since the early 2000s. Lately, the use of differential privacy has seen a great demand thanks to the increased adoption of data science techniques by organisations. Differential privacy as technology has also been named in the 2020 Gartner Hype cycle.

With data comes responsibility. To protect the privacy of data providers is crucial. Be it population census or customer feedback on app stores; no company should be able to trace the source easily.

Differential privacy offers a mathematical framework to anonymise data. It is a high-assurance, analytic means of ensuring that use cases like these are addressed in a privacy-preserving manner.

1. Differential privacy aims to ensure that regardless of whether an individual record is included in the data or not, a query on the data returns approximately the same result. Therefore, we need to know what the maximum impact of an individual record could be. This will be determined by the highest, and the lowest possible value in the data set and is referred to as the sensitivity of the data. The higher the sensitivity, the more noise needs to be applied.?

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