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Title: Impact evaluation of clustering-based k-anonymity for recommendations
Author: Ros Martín, Miguel
Director: Casas Roma, Jordi
Salas Piñón, Julián
Others: Universitat Oberta de Catalunya
Keywords: k-anonimity
Issue Date: 15-Jan-2017
Publisher: Universitat Oberta de Catalunya
Abstract: SaNGreeA is a greedy and deterministic clustering algorithm for achieving kanonymous clusters on a labeled, undirected graph. It is nowadays a classic and the leading work in clustering based k-anonymity algorithms. It has a quadratic time complexity which makes it really slow for a reasonably big network (1 M nodes). Our project adapts SaNGreeA to make it scalable for a real world big network and specialises it to make sure the generated clusters are useful for a recommender system.
Language: English
Appears in Collections:Bachelor thesis, research projects, etc.

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