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Authors: Julián Salas 1 and Vicenç Torra 2

Affiliations: 1 Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain, Center for Cybersecurity Research of Catalonia, Spain ; 2 Hamilton Institute, Maynooth University, Ireland, University of Skövde, Sweden

Keyword(s): Noise-graph Addition, Randomized Response, Edge Differential Privacy, Collaborative Filtering.

Abstract: Several methods for providing edge and node-differential privacy for graphs have been devised. However, most of them publish graph statistics, not the edge-set of the randomized graph. We present a method for graph randomization that provides randomized response and allows for publishing differentially private graphs. We show that this method can be applied to sanitize data to train collaborative filtering algorithms for recommender systems. Our results afford plausible deniability to users in relation to their interests, with a controlled probability predefined by the user or the data controller. We show in an experiment with Facebook Likes data and psychodemographic profiles, that the accuracy of the profiling algorithms is preserved even when they are trained with differentially private data. Finally, we define privacy metrics to compare our method for different parameters of ε with a k-anonymization method on the MovieLens dataset for movie recommendations.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Salas, J. and Torra, V. (2020). Differentially Private Graph Publishing and Randomized Response for Collaborative Filtering. In Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT; ISBN 978-989-758-446-6; ISSN 2184-7711, SciTePress, pages 415-422. DOI: 10.5220/0009833804150422

@conference{secrypt20,
author={Julián Salas. and Vicen\c{C} Torra.},
title={Differentially Private Graph Publishing and Randomized Response for Collaborative Filtering},
booktitle={Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT},
year={2020},
pages={415-422},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009833804150422},
isbn={978-989-758-446-6},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on e-Business and Telecommunications - SECRYPT
TI - Differentially Private Graph Publishing and Randomized Response for Collaborative Filtering
SN - 978-989-758-446-6
IS - 2184-7711
AU - Salas, J.
AU - Torra, V.
PY - 2020
SP - 415
EP - 422
DO - 10.5220/0009833804150422
PB - SciTePress