Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/16059
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 High-dimensional Personal information Re identifications Sanitization 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 |
URI: | http://hdl.handle.net/10773/16059 |
ISSN: | 1888-5063 |
Publisher Version: | http://www.tdp.cat/issues11/abs.a202a14.php |
Appears in Collections: | DETI - Artigos |
Files in This Item:
File | Description | Size | Format | |
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tdp.a202a14.pdf | 345.06 kB | Adobe PDF | View/Open |
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