Differential privacy is known as the technique in the digital tech sector, aimed at enabling businesses to process & disclose their audience data to third parties, when needed, while securing individual End Users’ Personal Data.
What Is Differential Privacy?
From a technical standpoint, differential privacy implies the audience data input is being saturated with the so-called “data noise”, in order to eliminate or at least minimize risks of ever disclosing sensitive Personal Data elements.
Unlike other privacy preserving approaches, the idea behind differential privacy is that the amount of added “noise” is mathematically calibrated in order to sustain the quality of the aggregated data output, while protecting the actual individual’s Personal Data.
Frequent Use Cases
Some of the frequent use cases of differential privacy in digital advertising tech landscape include the following:
- measurement & analytics (e.g. for evaluating the effectiveness of online video ads);
- audience discovery (e.g. for getting audience behavior & engagement insights – see LinkedIn’s Audience Engagement API);
- audience behavior modeling (e.g. for assessment and improvement of browser behavior, mobile typing suggestions and other feature enhancements), and more.
Potential Challenges
One of the major requirements, while implementing differential privacy, is the sufficiency of volume of the aggregated data input prior to data noise injection. In this respect, while it’s also possible to apply this approach to smaller datasets prior to aggregation, the quality of the data output may be too saturated to remain applicable for analytics purposes.
Correspondingly, whether or not the data input includes individual Personal Data elements, the use of differential privacy makes it challenging (if even possible) to obtain the quality data output on the individual level.
In this sense, while differential privacy can prove a practically helpful approach for achieving particular business goals and outcomes (e.g. release of the annual statistics reports), its use should be perceived in tandem with other techniques, all designed to help digital companies conduct efficient audience measurement analytics and improve the overall effectiveness of their marketing & advertising initiatives, hence securing desired revenue results.