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Reconciling personalization with privacy has been a continuing interest in the user modeling community. In prior work, the authors proposed a dynamic privacy-enhancing user modeling framework based on a software Product Line Architecture (PLA). The system dynamically selects personalization methods during runtime that respect users' current privacy preferences as well as the prevailing privacy laws and regulations. One major concern about the approach is its performance since dynamic architectural reconfiguration during runtime is usually resource-intensive. In this paper, they describe four implementations of the system that vary two factors, and an in-depth performance evaluation thereof under realistic workload conditions.
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