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.