Co-authored by Arvind Narayanan.
Measurement is central to online advertising: it facilitates billing, performance measurement, targeting decisions, spending allocation, and more. In a pair of earlier posts we explained how advertisement frequency capping and behavioral targeting are achievable without compiling a user’s browsing history. This post similarly proposes practical, privacy-improved approaches to advertising measurement.
Users hold widely varying preferences on web tracking.1 Some don’t mind the practice. Some object to it entirely. Many trust certain organizations to follow them around the web.
Do Not Track accomodates these divergent preferences in two ways. First, browsers and other user agents include an option for universally signaling a preference against tracking (“DNT: 1”). Firefox, Internet Explorer, and Safari have all integrated this feature, and Chrome will support it by the end of the year. Second, a user can configure exceptions to the universal signal. Some websites may choose to build a proprietary “out-of-band” exception mechanism, using ordinary web technologies, that trumps the “DNT: 1” signal. The Do Not Track Cookbook includes an example of how a Facebook out-of-band exception mechanism might appear.
The W3C Do Not Track standard will provide another option: a simple JavaScript interface that allows a website to request an exception, paired with a signal that some tracking is allowed (“DNT: 0”).
Original at 33 Bits of Entropy.
Co-authored by Arvind Narayanan and Subodh Iyengar.
In the first installment of the Tracking Not Required series, we discussed a relatively straightforward case: frequency capping. Now let’s get to the 800-pound gorilla, behaviorally targeted advertising, putatively the main driver of online tracking. We will show how to swap a little functionality for a lot of privacy.
Admittedly, implementing behavioral targeting on the client is hard and will require some technical wizardry. It doesn’t come for “free” in that it requires a trade-off in terms of various privacy and deployability desiderata. Fortunately, this has been a fertile topic of research over the past several years, and there are papers describing solutions at a variety of points on the privacy-deployability spectrum. This post will survey these papers, and propose a simplification of the Adnostic approach — along with prototype code — that offers significant privacy and is straightforward to implement.
Co-authored by Arvind Narayanan.
Debates over web tracking and Do Not Track tend to be framed as a clash between consumer privacy and business need. That’s not quite right. There is, in fact, a spectrum of possible tradeoffs between business interests and consumer privacy.
Our aim with the Tracking Not Required series is to show how those tradeoffs are not at all linear; it is possible to swap a little functionality for a lot of privacy. We only use technologies that are already deployed in browsers, and the solutions we propose are externally verifiable.1
We focus on issues at the center of Do Not Track negotiations in the World Wide Web Consortium. Advertising companies have pledged to stop forms of ad targeting once a user enables Do Not Track, but many maintain that tracking is essential for a litany of “operational uses.” The Tracking Not Required series demonstrates how business functionality can be implemented without exposing users to the risks of tracking.
This first post addresses frequency capping in online advertising, the most frequently cited “operational use” necessitating tracking.
