The Science Behind Rapleaf’s Anonymization

Here is a blog post from our awesome engineering team that discusses how we’re working on anonymizing large amounts of data, which is the backbone of our display advertising solution. While the blog entry delves into some technical details, it provides a good overview of our vision and focus on safeguarding information.

Privacy is an incredibly important issue to us at Rapleaf; it informs all our business and engineering decisions. Occasionally, privacy concerns can lead us to some really interesting engineering challenges. We love this: not only do we get to work on protecting our users’ privacy, we also get a chance to tackle ridiculously challenging problems-stuff no one else is working on. It’s a win-win situation. One recent effort that exemplifies this attitude at Rapleaf is our Anonymouse project.

The Problem

Operating our Rapleaf Display Media product involves dropping cookies on users’ browsers. These cookies contain various tidbits of information about the user-basic demographics, interest data, and the like. Using these cookies, we make it possible for content providers to customize their websites to individual users. It’s pretty exciting stuff, and we think that it’ll change the face of the web.

In privacy circles, there’s a concept known as Personally Identifiable Information(or PII). PII refers to data that can be uniquely linked to a specific individual: things like name, address, phone number, or email. It goes without saying that we don’t drop any PII in our cookies. But it’s become abundantly clear that simply stripping your dataset of PII is not enough to make it anonymous.