User privacy on the internet is an important and unsolved problem. So far, no sufficient and comprehensive solution has been proposed that helps a user to protect his or her privacy while using the internet. Data is collected and assembled by numerous service providers. Solutions so far focused on the side of the service providers to store encrypted or transformed data that can be still used for analysis. This has a major flaw, as it relies on the service providers to do this. The user has no chance of actively protecting his or her privacy. In this work, we suggest a new approach, giving the user the same tool the other side has, namely data mining techniques to produce data which obfuscates the user’s identity. We apply this approach to search engine queries and use feedback of the search engines in terms of personalized advertisements in an algorithm inspired by reinforcement learning to generate new queries potentially confusing the search engine. We evaluated the approach using a real-world data set. While evaluation is a hard task, we achieve promising results that indicate that it is possible to influence the user’s prof i le that the search engine generates. This shows that it is feasible to defend a user’s privacy from a new and more practical perspective.
Data Mining and Knowledge Discovery, 2017, ISSN: 1573-756X.