User-adaptive (or "personalized") systems on the web cater their interaction to each individual user and provide considerable benefits to both users and web vendors. These systems pose privacy problems, however, since they must collect large amounts of personal information to be able to adapt to users, and often do this in a rather inconspicuous manner. The interaction with personalized systems is therefore likely to be affected by users' privacy concerns, and is in many cases also subject to privacy laws and self-regulatory privacy principles. An analysis of nearly 30 international privacy laws revealed that many of them impose severe restrictions not only on the data that may be collected but also on the personalization methods that may be employed. For many personalization goals, more than one methods can be used that differ in their data and privacy requirements and their anticipated accuracy and reliability. This paper presents a software architecture that encapsulates the different personalization methods in individual components and, at any point during runtime, ascertains the dynamic selection of the component with the optimal anticipated personalization effects among those that are permissible under the currently prevailing privacy constraints.
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