Paper 2023/1048
An Algorithm for Persistent Homology Computation Using Homomorphic Encryption
Abstract
Topological Data Analysis (TDA) offers a suite of computational tools that provide quantified shape features in high dimensional data that can be used by modern statistical and predictive machine learning (ML) models. In particular, persistent homology (PH) takes in data (e.g., point clouds, images, time series) and derives compact representations of latent topological structures, known as persistence diagrams (PDs). Because PDs enjoy inherent noise tolerance, are interpretable and provide a solid basis for data analysis, and can be made compatible with the expansive set of well-established ML model architectures, PH has been widely adopted for model development including on sensitive data, such as genomic, cancer, sensor network, and financial data. Thus, TDA should be incorporated into secure end-to-end data analysis pipelines. In this paper, we take the first step to address this challenge and develop a version of the fundamental algorithm to compute PH on encrypted data using homomorphic encryption (HE).
Metadata
- Available format(s)
-
PDF
- Category
- Public-key cryptography
- Publication info
- Preprint.
- Keywords
- homomorphic encryptiontopological data analysissecure computingpersistent homologyprivacy enhancing technology
- Contact author(s)
-
dgold2012 @ fau edu
koray karabina @ nrc-cnrc gc ca
fmotta @ fau edu - History
- 2023-07-05: approved
- 2023-07-04: received
- See all versions
- Short URL
- https://ia.cr/2023/1048
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1048,
author = {Dominic Gold and Koray Karabina and Francis C. Motta},
title = {An Algorithm for Persistent Homology Computation Using Homomorphic Encryption},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/1048},
year = {2023},
url = {https://eprint.iacr.org/2023/1048}
}
