Accelerating CKKS Homomorphic Encryption with Data Compression on GPUs

Quoc Bao Phan, Linh Nguyen, Tuy Tan Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Homomorphic encryption (HE) algorithms, particularly the Cheon-Kim-Kim-Song (CKKS) scheme, offer significant potential for secure computation on encrypted data, making them valuable for privacy-preserving machine learning. However, high latency in large integer operations in the CKKS algorithm hinders the processing of large datasets and complex computations. This paper proposes a novel strategy that combines lossless data compression techniques with the parallel processing power of graphics processing units to address these challenges. Our approach demonstrably reduces data size by 90% and achieves significant speedups of up to 100 times compared to conventional approaches. This method ensures data confidentiality while mitigating performance bottlenecks in CKKS-based computations, paving the way for more efficient and scalable HE applications.

Original languageEnglish (US)
Title of host publication2024 IEEE 67th International Midwest Symposium on Circuits and Systems, MWSCAS 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1145-1149
Number of pages5
ISBN (Electronic)9798350387179
DOIs
StatePublished - 2024
Externally publishedYes
Event67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024 - Springfield, United States
Duration: Aug 11 2024Aug 14 2024

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference67th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2024
Country/TerritoryUnited States
CitySpringfield
Period8/11/248/14/24

Keywords

  • CKKS
  • data compression
  • graphics processing units
  • Homomorphic encryption
  • privacy-preserving

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Accelerating CKKS Homomorphic Encryption with Data Compression on GPUs'. Together they form a unique fingerprint.

Cite this