A Software Product Line Approach for Handling Privacy Constraints in Web Personalization

Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits are severely counteracted by privacy concerns. Personalized systems need to take these into account, as well as privacy laws and industry self-regulations that may be in effect. When these constraints are present, they not only affect the personal data that can be collected, but also the methods that can be used to process the data. The present research aims at maximizing the personalization benefits, while at the same time satisfying the currently prevailing privacy constraints. Since such privacy constraints can change over time, we seek a systematic and flexible mechanism that can cater to this dynamics. We looked at several existing approaches and found that they fail to present a practical and efficient solution. Inspired by the ability of software product lines to support software variability, we propose a user modeling architecture based thereon that supports architectural level configuration management to dynamically select personalization methods that satisfy current privacy constraints. A pilot experiment is being carried out with the support of an existing user modeling server and a software architecture based development environment.