Improving Locally Differentially Private Graph Statistics Through Sparseness-Preserving Noise-Graph Addition

Sudipta Paul, Julián Salas, Vicenç Torra

2025

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.

Paper Citation

in Harvard Style

Paul S., Salas J. and Torra V. (2025). Improving Locally Differentially Private Graph Statistics Through Sparseness-Preserving Noise-Graph Addition. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 526-533. DOI: 10.5220/0013174400003899

in Bibtex Style

@conference{icissp25,
author={Sudipta Paul and Julián Salas and Vicenç Torra},
title={Improving Locally Differentially Private Graph Statistics Through Sparseness-Preserving Noise-Graph Addition},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={526-533},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013174400003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Improving Locally Differentially Private Graph Statistics Through Sparseness-Preserving Noise-Graph Addition
SN - 978-989-758-735-1
AU - Paul S.
AU - Salas J.
AU - Torra V.
PY - 2025
SP - 526
EP - 533
DO - 10.5220/0013174400003899
PB - SciTePress