Make some room for the zeros: Data sparsity in secure distributed machine learning

Phillipp Schoppmann, Mariana Raykova, Adrià Gascón, Benny Pinkas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Exploiting data sparsity is crucial for the scalability of many data analysis tasks. However, while there is an increasing interest in efficient secure computation protocols for distributed machine learning, data sparsity has so far not been considered in a principled way in that setting. We propose sparse data structures together with their corresponding secure computation protocols to address common data analysis tasks while utilizing data sparsity. In particular, we define a Read-Only Oblivious Map primitive (ROOM) for accessing elements in sparse structures, and present several instantiations of this primitive with different trade-offs. Then, using ROOM as a building block, we propose protocols for basic linear algebra operations such as Gather, Scatter, and multiple variants of sparse matrix multiplication. Our protocols are easily composable by using secret sharing. We leverage this, at the highest level of abstraction, to build secure protocols for non-parametric models (k-nearest neighbors and naive Bayes classification) and parametric models (logistic regression) that enable secure analysis on high-dimensional datasets. The experimental evaluation of our protocol implementations demonstrates a manyfold improvement in the efficiency over state-of-the-art techniques across all applications. Our system is designed and built mirroring the modular architecture in scientific computing and machine learning frameworks, and inspired by the Sparse BLAS standard.

Original languageEnglish
Title of host publicationCCS 2019 - Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages1335-1350
Number of pages16
ISBN (Electronic)9781450367479
DOIs
StatePublished - 6 Nov 2019
Event26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019 - London, United Kingdom
Duration: 11 Nov 201915 Nov 2019

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019
Country/TerritoryUnited Kingdom
CityLondon
Period11/11/1915/11/19

Bibliographical note

Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

  • Machine learning
  • Secure computation
  • Sparsity

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