Accelerating CKKS Homomorphic Encryption with Data Compression on GPUs

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 hinde...

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Bibliographic Details
Main Authors: Phan, Quoc Bao, Nguyen, Linh, Nguyen, Tuy Tan
Format: Conference Proceeding
Language:English
Subjects:
CKKS
Data compression
Data privacy
Graphics processing units
Homomorphic encryption
Machine learning
Machine learning algorithms
Parallel processing
privacy-preserving
System performance
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Summary: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.
ISSN:1558-3899
DOI:10.1109/MWSCAS60917.2024.10658747