Reconciling personalization with privacy has been a continuing interest in user modeling research. This aim has computational, legal and behavioral/attitudinal ramifications. We present a dynamic privacy-enhancing user modeling framework that supports compliance with users' personal privacy preferences and with the privacy laws and regulations that apply to them. The framework is based on a software product line architecture. It dynamically selects personalization methods during runtime that

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... t the current privacy constraints. Since dynamic architectural reconfiguration is typically resource-intensive, we conducted a performance evaluation with four implementations of our system that vary two factors. The results demonstrate that at least one implementation of our approach is technically feasible with comparatively modest additional resources, even for web sites with the highest traffic today. To gauge user reactions to privacy controls that our framework enables, we also conducted a controlled experiment that allowed one group of users to specify privacy preferences and view the resulting effects on employed personalization methods. We found that users in this treatment group utilized this feature, deemed it useful, and had fewer privacy concerns as measured by higher disclosure of their personal data.