Performance Evaluation of a Privacy-Enhancing Framework for Personalized Websites

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 less than 0.2 seconds for all versions except the original PLA implementation. Overall, our results demonstrate that with a reasonable number of networked hosts in a cloud computing environment, an internationally operating website can use our dynamic PLA-based user modeling approach to personalize their user services, and at the same time respect the individual privacy desires of their users as well as the privacy norms that may apply.

This research has been supported through NSF grant IIS 0308277 and a Google Research Award. We would like to thank Scott Hendrickson, Eric Dashofy, André van der Hoek and the UMAP09 reviewers for their helpful comments.