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Poster: computing the persistent homology of encrypted data

From National Research Council Canada

Alternative titleComputing the persistent homology of encrypted data
DOIResolve DOI: https://doi.org/10.1145/3576915.3624406
AuthorSearch for: ORCID identifier: https://orcid.org/0009-0005-5783-1766; Search for: 1ORCID identifier: https://orcid.org/0000-0001-9538-8877; Search for: ORCID identifier: https://orcid.org/0000-0002-0364-5440
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Poster
ConferenceCCS '23: ACM SIGSAC Conference on Computer and Communications Security, November 26-30, 2023, Copenhagen Denmark
Subjecthomomorphic encryption; topological data analysis; secure computing; persistent homology
Abstract
Topological Data Analysis (TDA) offers a suite of computational tools that provide quantified shape features of high dimensional data that can be used by modern statistical and predictive machine learning (ML) models. Persistent homology (PH) transforms data (e.g., point clouds, images, time series) into persistence diagrams (PDs)--compact representations of its latent topological structures. Because PDs enjoy inherent noise tolerance, are interpretable, 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. Thus, TDA should be incorporated into secure end-to-end data analysis pipelines. This paper introduces a version of the fundamental algorithm to compute PH on encrypted data using homomorphic encryption (HE).
Publication date
PublisherACM
In
LanguageEnglish
Peer reviewedYes
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Record identifier64dba46d-321f-45f7-a2ae-4e320f7ba014
Record created2024-04-18
Record modified2024-04-18
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