Privacy Roundtables - Comment, Project No. P095416' #544506-00017

Submission Number:
544506-00017
Commenter:
Paul Francis
Organization:
Max Planck Institute for Software Systems
State:
Outside the United States
Initiative Name:
Privacy Roundtables - Comment, Project No. P095416'

This comment relates to privacy in behavioral advertising. This comment comes from the Privad research group at the Max Planck Institute for Software Systems in Germany (www.mpi-sws.org), and the Adnostic research group situated at NYU and Stanford University. We believe that there exist technical approaches to behavioral advertising that protect user privacy while still allowing good targeting, and that fit within the business models for online advertising that have evolved in recent years. Specifically, we would like to bring to the attention of the roundtable two research projects in private online advertising. One, from MPI-SWS, is called Privad (see http://adresearch.mpi-sws.org/). The other, from Stanford and NYU, is called Adnostic (formerly Privads, see http://crypto.stanford.edu/adnostic/). While these two approaches differ in a number of details, they both share the characteristic that user profiling information is stored only on the user's own computer. This is in contrast to today's approaches, where user profiling is stored in the "cloud" (e.g. Google's data centers). Privad and Adnostic radically change the privacy equation for behavioral advertising. Today ad networks and users are locked in a privacy arms race. Ad networks invent increasingly sophisticated ways to track users from afar, while users look for tools that protect them. In the meantime, privacy advocates and regulators ask ad networks to restrain themselves, with limited effect. The promise of Privad or Adnostic is that ad networks can continue making money brokering ads without having to gather and store user profiling information. This is win-win for users and ad networks alike: it protects user privacy and releases ad networks from the responsibility of having to protect private user information. We are delighted at the opportunity to contribute our expertise to the public debate on privacy in behavioral advertising. Please let us know how we may be of further assistance. Solon Barocas, NYU, solon@nyu.edu Dan Boneh, Stanford U, dabo@cs.stanford.edu Paul Francis, MPI-SWS, francis@mpi-sws.org Saikat Guha, MPI-SWS, sguha@mpi-sws.org Arvind Narayanan, Stanford U, randomwalker@gmail.com Helen Nissenbaum, NYU, hfn1@nyu.edu Vincent Toubiana, NYU, v.toubiana@free.fr