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.
['Alegria Baquero', 'Allan M. Schiffman', 'Jeff Shrager']
Blend me in: Privacy-preserving input generalization for personalized online services