A Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

L.B. Pulido-Gaytan, A. Tchernykh, J.M. Cortés-Mendoza, M. Babenko, South State

Research output: Conference proceeding/Chapter in Book/Report/Conference Paperpeer-review

Abstract

The secure and efficient processing of private information in the cloud computing paradigm is still an open issue. New security threats arise with the increasing volume of data into cloud storage, where cloud providers require high levels of trust, and data breaches are significant problems. Encrypting the data with conventional schemes is considered the best option to avoid security problems. However, a decryption process is necessary when the data must be processed, but it falls into the initial problem of data vulnerability. The user cannot operate on the data directly and must download it to perform the computations locally. In this context, Fully Homomorphic Encryption (FHE) is considered the holy grail of cryptography in order to solve cybersecurity problems, it allows a non-trustworthy third-party resource to blindly process encrypted information without disclosing confidential data. FHE is a valuable capability in a world of distributed computation and heterogeneous networking. In this survey, we present a comprehensive review of theoretical concepts, state-of-the-art, limitations, potential applications, and development tools in the domain of FHE. Moreover, we show the intersection of FHE and machine learning from a theoretical and a practical point of view and identify potential research directions to enrich Machine Learning as a Service, a new paradigm of cloud computing. Specifically, this paper aims to be a guide to researchers and practitioners interested in learning, applying, and extending knowledge in FHE over machine learning. © 2021, Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationLatin American High Performance Computing Conference
Subtitle of host publicationCARLA 2020: High Performance Computing
Pages115-129
Number of pages15
Volume1327
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event7th Latin American High Performance Computing Conference - Cuenca, Ecuador
Duration: 2 Sept 20204 Sept 2020

Conference

Conference7th Latin American High Performance Computing Conference
Abbreviated titleCARLA 2020
Country/TerritoryEcuador
CityCuenca
Period2/09/204/09/20

Keywords

  • Cloud security
  • Fully homomorphic encryption
  • Machine learning as a service
  • Cloud computing
  • Digital storage
  • Machine learning
  • Privacy by design
  • Surveys
  • Trusted computing
  • Conventional schemes
  • Distributed computations
  • Encrypted informations
  • Heterogeneous networking
  • Potential researches
  • Privacy preserving
  • Private information
  • Cryptography

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