Authors:
Sudipta Paul
1
;
Julián Salas
2
and
Vicenç Torra
1
Affiliations:
1
Department of Computing Science, Umeå Universitet, Umeå, Sweden
;
2
Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Bellaterra, Spain
Keyword(s):
Privacy in Large Network, Differential Privacy, Edge Local Differential Privacy.
Abstract:
Differential privacy allows to publish graph statistics in a way that protects individual privacy while still allowing meaningful insights to be derived from the data. The centralized privacy model of differential privacy assumes that there is a trusted data curator, while the local model does not require such a trusted authority. Local differential privacy is commonly achieved through randomized response (RR) mechanisms. This does not preserve the sparseness of the graphs. As most of the real-world graphs are sparse and have several nodes, this is a drawback of RR-based mechanisms, in terms of computational efficiency and accuracy. We thus, propose a comparative analysis through experimental analysis and discussion, to compute statistics with local differential privacy, where, it is shown that preserving the sparseness of the original graphs is the key factor to gain that balance between utility and privacy. We perform several experiments to test the utility of the protected graphs
in terms of several sub-graph counting i.e. triangle, and star counting and other statistics. We show that the sparseness preserving algorithm gives comparable or better results in comparison to the other state of the art methods and improves computational efficiency.
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