Performance Evaluation of a Privacy-Enhancing Framework for Personalized Websites

13 years 10 months ago

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Abstract. Reconciling personalization with privacy has been a continuing interest in the user modeling community. In prior work, we proposed a dynamic privacy-enhancing user modeling framework based on a software product line architecture (PLA). Our 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 our approach is its performance since dynamic architectural reconﬁguration during runtime is usually resource-intensive. In this paper, we describe four implementations of our system that vary two factors, and an in-depth performance evaluation thereof under realistic workload conditions. Our study shows that a customized version performs better than the original PLA implementation, that a multi-level caching mechanism improves both versions, and that the customized version with caching performs best. The average handling time per user session is...

Yang Wang 0005, Alfred Kobsa

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