|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Privacy in Advertising: Not all Adware is Badware
If you have a question about this talk, please contact Mateja Jamnik.
Online advertising is a major economic force in the Internet today. Today’s deployments, however, increasingly erode user privacy as advertising companies like Google increasingly target users. In this talk, we suggest that it is possible to build an advertising system that fits well into the existing online advertising business model, targets users extremely well, is very private, and scales well. The key to our approach is adware: client computers run a local software agent that profiles the user, requests the appropriate ads, displays the locally, and reports on views and clicks, all while preserving privacy. This talk outlines the system design, and discusses its pros and cons.
Paul Francis is a tenured faculty at the Max Planck Institute for Software Systems in Germany. Paul has held research positions at Cornell University, ACIRI , NTT Software Labs, Bellcore,and MITRE , and was Chief Scientist at two Silicon Valley startups. Paul’s research centers around routing and addressing problems in the Internet and P2P networks. Paul’s innovations include NAT , shared-tree multicast, the first P2P multicast system, the first DHT (as part of landmark routing), and Virtual Aggregation. These days, Paul is wondering why so much of our private data is being held in the cloud.
This talk is part of the Computer Laboratory Wednesday Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsConservation seminars Social Enterprise and International Development Institute of Astronomy Extra Talks
Other talksHow parasites adapt their shape to different hosts Salt fluxes from sea ice: simple models of reactively dissolved channels Regulation of mitochondrial function by stearic acid Saved by the British Empire: how the US escaped the Great Depression A Bayesian nonparametric approach to testing for dependence between random variables tba