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Two-phase preference disclosure in attributed social networks

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Abdessamad Imine
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Amedeo Napoli
Chedy Raïssi

Abstract

In order to demonstrate privacy threats in social networks we show how to infer user preferences by random walks in a multiple graph representing simultaneously attributes and relationships links. For the approach to scale in a rst phase we reduce the space of attribute values by partition in balanced homogeneous clusters. Following the Deepwalk approach, the random walks are considered as sentences. Hence unsu-pervised learning techniques from natural languages processing can be employed in second phase to deduce semantic similarities of some attributes. We conduct initial experiments on real datasets to evaluate our approach.
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Dates and versions

hal-01649246 , version 1 (27-11-2017)

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Younes Abid, Abdessamad Imine, Amedeo Napoli, Chedy Raïssi, Michaël Rusinowitch. Two-phase preference disclosure in attributed social networks. DEXA 2017 - 28th International Conference on Database and Expert Systems Applications , Aug 2017, Lyon, France. pp.249-263, ⟨10.1007/978-3-319-64468-4_19⟩. ⟨hal-01649246⟩
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