Privacy-Preserving Algorithms for Data Cooperatives with Directed Graphs

Mark Dockendorf, Ram Dantu

2023

Abstract

A handful of companies currently hold large collections of data about most people. In addition to the questionable ethics of collecting personal data with few-to-no options to limit what these companies collect, there exist exceptionally few ways to regulate how your data is stored and used once it is collected. Furthermore, these data collections cannot be easily cross-referenced to gain insight. Data cooperatives provide an alternative to these separated collections of data. As a participant-driven organization, similar to a credit union, data cooperatives have a vested interest in preserving the privacy of individuals while offering insight similar to other big data analytics. Another bonus of the data cooperative model is the voluntary (and ethical) sourcing of data. The downside of giving participants the freedom to choose which data they contribute is incomplete data sets. To help address this, we adapt label propagation, a semi-supervised learning algorithm for community detection based on partially labeled data, to work over homomorphically encrypted (HE) graphs. We also adapt triangle counting and a vertex scoring scheme to work over directed heterogeneous-vertex, heterogeneous-edge HE graph data.

Paper Citation

in Harvard Style

Dockendorf M. and Dantu R. (2023). Privacy-Preserving Algorithms for Data Cooperatives with Directed Graphs. In Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-666-8, SciTePress, pages 876-884. DOI: 10.5220/0012140200003555

in Bibtex Style

@conference{secrypt23,
author={Mark Dockendorf and Ram Dantu},
title={Privacy-Preserving Algorithms for Data Cooperatives with Directed Graphs},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2023},
pages={876-884},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012140200003555},
isbn={978-989-758-666-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - Privacy-Preserving Algorithms for Data Cooperatives with Directed Graphs
SN - 978-989-758-666-8
AU - Dockendorf M.
AU - Dantu R.
PY - 2023
SP - 876
EP - 884
DO - 10.5220/0012140200003555
PB - SciTePress