A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption

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Abstract

Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving Machine Learning (PPML) methods have been proposed to mitigate the privacy and security risks of ML models. A popular approach to achieving PPML uses Homomorphic Encryption (HE). However, the highly publicized inefficiencies of HE make it unsuitable for highly scalable scenarios with resource-constrained devices. Hence, Hybrid Homomorphic Encryption (HHE) – a modern encryption scheme that combines symmetric cryptography with HE – has recently been introduced to overcome these challenges. HHE potentially provides a foundation to build new efficient and privacy-preserving services that transfer expensive HE operations to the cloud. This work introduces HHE to the ML field by proposing resource-friendly PPML protocols for edge devices. More precisely, we utilize HHE as the primary building block of our PPML protocols. We assess the performance of our protocols by first extensively evaluating each party’s communication and computational cost on a dummy dataset and show the efficiency of our protocols by comparing them with similar protocols implemented using plain BFV. Subsequently, we demonstrate the real-world applicability of our construction by building an actual PPML application that uses HHE as its foundation to classify heart disease based on sensitive ECG data.
Original languageEnglish
Title of host publication2024 IEEE Conference on Dependable, Autonomic and Secure Computing (DASC)
PublisherIEEE
Pages47-56
Number of pages10
ISBN (Electronic)979-8-3315-2272-8
ISBN (Print)979-8-3315-2273-5
DOIs
Publication statusPublished - 5 Nov 2024
Publication typeA4 Article in conference proceedings
EventIEEE Conference on Dependable, Autonomic and Secure Computing - Boracay Island, Philippines
Duration: 5 Nov 20248 Nov 2024

Publication series

NameIEEE International Conference on Dependable, Autonomic and Secure Computing
ISSN (Print)2837-0724
ISSN (Electronic)2837-0740

Conference

ConferenceIEEE Conference on Dependable, Autonomic and Secure Computing
Country/TerritoryPhilippines
CityBoracay Island
Period5/11/248/11/24

Keywords

  • Privacy-Preserving Machine Learning
  • machine learning as a service
  • Hybrid Homomorphic Encryption

Publication forum classification

  • Publication forum level 1

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