Michael Dr. Marhöfer

Privacy-preserving data mining demonstrator

We present a system for demonstrating anonymized data mining on user profiles, which also evaluates the remaining utility of the generalized information through comparison of the classification results. The use case for this scenario builds around profiles containing personally identifiable information, which should be analyzed and classified by a third party. Our goal is to preserve the privacy of the users while maintaining a certain level of utility to allow data mining over the anonymized information.

Do-Not-Track Techniques for Browsers and Their Implications for Consumers

Recent efforts to increase online privacy by offering the user a more general choice to opt out of online tracking were mainly pushed by the FTC in late 2010. As the FTC explicitly omitted technical details, browser developers started to implement what they thought might be appropriate to either limit user tracking directly, or let the advertiser know about the user’s wish to not be tracked. This paper gives a short overview on the positions and arguments of stakeholders and evaluates the technical proposals and implementations aiming to support the consumer in keeping control over his personal data.