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Facebook ads are investing in confidentiality research to improve tracking

Facebook ads announce targeting updates, Instagram store ads, and more

Facebook announced that it is investing in research that supports the promotion of Privacy Enhancing Technology through academics, global organizations and developers.

It’s no surprise that Facebook is very interested in privacy enhancement technologies after the changes from iOS14 have created huge data loss for advertisers. The goal of investing in these technologies is to identify better tracking methods that will preserve privacy while bringing anonymized and aggregated data back to Facebook, which improves advertiser performance.

Privacy and Facebook ads

Privacy-Enhancing Technologies (PET) are technologies that can minimize the amount of data processed for the purpose of protecting personal information.


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Facebook shared examples of tracking Covid contact and sending electronic payments to illustrate how PET can be used to track information needed to know while protecting personal data.

There are three primary PETs that Facebook invests in, which include: Multiparty Computing, On-Device Learning and Differential Privacy. Let’s dive into each of them.

Multiparty calculation (MPC) and measurement of private lifts

Facebook has tested a solution called Private Lift Measurement, which uses multi-party computation (MPC) to help advertisers understand performance while keeping consumer data private.

MPCs are used to calculate results using data sources from multiple parties. For example, in the Facebook use case, this type of reporting is used to combine ad engagement data from one party and purchase data from another.


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Facebook expects this measurement to be available to all advertisers next year, but has so far opened up the framework so that any developer can create privacy-centric measurement products using MPC.

On-Device Learning

On-Device Learning is just like the sound-tracking that lives in the individual device, which then trains an algorithm about certain habits and likely future behaviors.

For example, Facebook provides the example that if people click on exercise equipment also tend to buy protein shakes, learning on the device would detect these patterns without sending this individual data to the cloud.

This sounds like something similar to what Google Chrome is trying to achieve with FLoC by keeping browser data in the individual browser.

Differential privacy

Last but not least, differential confidentiality calculates noise in a data set. It anonymizes the data by making small changes to it, to make it more difficult to know exactly who performed a particular action.

This technology is therefore often used for public research for that reason. Differential privacy can be used alone or with other technologies that enhance privacy.

When can we expect to see changes?

Facebook did not provide an exact timeline for when changes were expected, but they mentioned that the initiative is a multi-year effort. Presumably, they will start testing things within this time frame, but advertisers may not see major changes in the near future.


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