Blend me in: Privacy-preserving input generalization for personalized online services

Users routinely disclose personal information to obtain the benefits of Personalized Online Services. As a result, personal data is distributed across uncounted and unaccountable remote databases. Data mismanagement, as well as privacy and security flaws undermine individuals´ control and privacy of their personal data. Yet revealing detailed private data does not necessarily yield useful service personalization; often this functionality is only modestly dependent upon the accuracy of user-supplied input. We demonstrate knowledge-based input generalization wherein systematically perturbed user data is supplied to a personalized service to gain forward privacy for the user, while retaining the utility of the service´s results.