Personalization vs. Privacy in Big Data Analysis

Abstract : Personalization is the process of adapting the output of a system to a user's context and profile. User information such as geographical location, academic and professional background, membership in groups, interests, preferences, opinions, etc. may be used in the process. Big data analysis techniques enable collecting accurate and rich information for user profiles in particular due to their ability to process unstructured as well as structured information in high volumes from multiple sources. Accurate and rich user profiles are important for personalization. For example, such data are required for recommender systems, which try to predict elements that a user has not yet considered. However, the information used for personalization can often be considered private, which raises privacy issues. In this paper, we discuss personalization with big data analysis techniques and the associated privacy challenges. We illustrate these aspects through the ongoing EEXCESS project. We provide a concrete example of a personalization service, proposed as part of the project, that relies on big data analysis techniques.
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Benjamin Habegger, Omar Hasan, Lionel Brunie, Nadia Bennani, Harald Kosch, et al.. Personalization vs. Privacy in Big Data Analysis. International Journal of Big Data, 2014, pp.25-35. ⟨hal-01270826⟩

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