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A PLA-based privacy-enhancing user modeling framework and its evaluation

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Abstract

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’ individual privacy preferences and with the privacy laws and regulations that apply to each user. The framework is based on a software product line architecture. It dynamically selects personalization methods during runtime that meet 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 websites 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.

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  1. Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA, 92697, USA

    Yang Wang & Alfred Kobsa

  2. School of Computer Science, Carnegie Mellon University, 4720 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Yang Wang

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  1. Yang Wang
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  2. Alfred Kobsa
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Correspondence to Yang Wang.

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The managing editor of this paper was Sandra Carberry, University of Delaware.

The conceptual and technical research described herein has already been reported in earlier preliminary publications, as indicated by self-references. The user evaluation and its connection with the other research thrusts is completely unreported.

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Wang, Y., Kobsa, A. A PLA-based privacy-enhancing user modeling framework and its evaluation. User Model User-Adap Inter 23, 41–82 (2013). https://doi.org/10.1007/s11257-011-9114-8

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