Below are examples of key observation data fields that might be collected to probabilistically infer identity.

The probabilistic identity graph technology forms likely associations of IDs (such as a Google Ad ID on Android, cookies on browsers, or IP on CTV and iOS) to the same device and devices to the same user or group. More importantly, this is done such that we don’t have in this system an understanding of which specific user (as identified by their member information) or group is involved.

The observation data collected (as described above) for probabilistic identity inferences is retained for 90 days.

Data collected is immediately pseudonymized. Due to the pseudonymization, the algorithms can at best learn which devices (and identifiers) likely belong to the same person (or device) or group and not who the individual is. Pseudonymized data in the graph processing line is highly confidential and strict access control policies are enforced.

This inference of identity is used to serve relevant ads on and off LinkedIn, measure the effectiveness of ads, and provide analytics that don't identify you.

As a member, you can opt out by using the respective settings that control each of the four purposes of our inference of identity: attribution, retargeting, analytics, and off-LinkedIn ads display. In addition, both members and non-members can visit the Digital Advertising Alliance opt-out page and check the LinkedIn opt-out box.