Performance evaluation of a privacy-enhancing framework for personalized websites

Yang Wang, Alfred Kobsa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationUser Modeling, Adaptation, and Personalization - 17th International Conference, UMAP 2009 formerly UM and AH, Proceedings
Pages78-89
Number of pages12
DOIs
StatePublished - Oct 15 2009
Event17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009 - Trento, Italy
Duration: Jun 22 2009Jun 26 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5535 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009
CountryItaly
CityTrento
Period6/22/096/26/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Wang, Y., & Kobsa, A. (2009). Performance evaluation of a privacy-enhancing framework for personalized websites. In User Modeling, Adaptation, and Personalization - 17th International Conference, UMAP 2009 formerly UM and AH, Proceedings (pp. 78-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5535 LNCS). https://doi.org/10.1007/978-3-642-02247-0_10