Please use this identifier to cite or link to this item:
Title: Privacy in data publishing for tailored recommendation scenarios
Author: Gonçalves, J. M.
Gomes, Diogo Nuno
Aguiar, R. L.
Keywords: Data anonymization and sanitization
High-dimensional datasets
Privacy-preserving data publishing
Rating prediction
Recommender systems
Tailored recommendations
Economic and social effects
Personal information
Re identifications
Sensitive attribute
Tailored recommendations
Data privacy
Issue Date: 2015
Publisher: IIIA-CSIC
Abstract: Personal information is increasingly gathered and used for providing services tailored to user preferences, but the datasets used to provide such functionality can represent serious privacy threats if not appropriately protected. Work in privacy-preserving data publishing targeted privacy guarantees that protect against record re-identification, by making records indistinguishable, or sensitive attribute value disclosure, by introducing diversity or noise in the sensitive values. However, most approaches fail in the high-dimensional case, and the ones that don’t introduce a utility cost incompatible with tailored recommendation scenarios. This paper aims at a sensible trade-off between privacy and the benefits of tailored recommendations, in the context of privacy-preserving data publishing. We empirically demonstrate that significant privacy improvements can be achieved at a utility cost compatible with tailored recommendation scenarios, using a simple partition-based sanitization method.
Peer review: yes
ISSN: 1888-5063
Publisher Version:
Appears in Collections:DETI - Artigos

Files in This Item:
File Description SizeFormat 
tdp.a202a14.pdf345.06 kBAdobe PDFView/Open

Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.