Graph Algorithms over Homomorphic Encryption for Data Cooperatives

Mark Dockendorf, Ram Dantu, John Long

2022

Abstract

“Big data” continues to grow in influence with few competitors able to challenge them. In order to slow the growth of and eventually replace these “data silos”, we must enable competition from alternative sources that respect users’ privacy, such as data cooperatives. In our previous work, we proposed an architecture for a privacy-preserving data cooperative that relies on homomorphic encryption (HE) to ensure data privacy and demonstrated ring-based BFS, degree centrality, and farness centrality over HE graph data. In this paper we expand our suite of HE graph algorithms to include single-source shortest-path, all-pairs shortest-path, minimum spanning tree, harmonic centrality, random walk, and betweenness centrality over HE graph data. These graph analysis algorithms support the core service of a data cooperative: to provide data and insights (or aggregates) to the service of the cooperative’s clients (researchers, companies, governments, etc.) while maintaining the privacy of their users.

Paper Citation

in Harvard Style

Dockendorf M., Dantu R. and Long J. (2022). Graph Algorithms over Homomorphic Encryption for Data Cooperatives. In Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT, ISBN 978-989-758-590-6, pages 205-214. DOI: 10.5220/0011277000003283

in Bibtex Style

@conference{secrypt22,
author={Mark Dockendorf and Ram Dantu and John Long},
title={Graph Algorithms over Homomorphic Encryption for Data Cooperatives},
booktitle={Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,},
year={2022},
pages={205-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011277000003283},
isbn={978-989-758-590-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Security and Cryptography - Volume 1: SECRYPT,
TI - Graph Algorithms over Homomorphic Encryption for Data Cooperatives
SN - 978-989-758-590-6
AU - Dockendorf M.
AU - Dantu R.
AU - Long J.
PY - 2022
SP - 205
EP - 214
DO - 10.5220/0011277000003283