TY - GEN
T1 - Accelerating Fully Homomorphic Encryption through Microarchitecture-Aware Analysis and Optimization
AU - Jung, Wonkyung
AU - Lee, Eojin
AU - Kim, Sangpyo
AU - Kim, Namhoon
AU - Lee, Keewoo
AU - Min, Chohong
AU - Cheon, Jung Hee
AU - Ahn, Jung Ho
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/3
Y1 - 2021/3
N2 - Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]-[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUS and FPGAS, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.
AB - Homomorphic Encryption (HE) [11] draws significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous FHE schemes [2]-[4], [8], [9], HE for Arithmetic of Approximate Numbers (HEAAN [3]), which is also known as CKKS (Cheon-Kim-Kim-Song), is rapidly gaining popularity [10] as it supports computation on real numbers. A critical shortcoming of HE is the high computational complexity of ciphertext arithmetic, especially, HE multiplication (HE Mul). For example, the execution time for computation on encrypted data (ciphertext) increases from 100s to 10,000s of times compared to that on native, unen-crypted messages. However, a large body of HE acceleration studies, including ones exploiting GPUS and FPGAS, lack a rigorous analysis of computational complexity and data access patterns of HE Mul with large parameter sets on CPUs, the most popular computing platform.
KW - Application analysis
KW - Homomorphic Encryption
KW - Multicore processing
UR - http://www.scopus.com/inward/record.url?scp=85105417694&partnerID=8YFLogxK
U2 - 10.1109/ISPASS51385.2021.00045
DO - 10.1109/ISPASS51385.2021.00045
M3 - Conference contribution
AN - SCOPUS:85105417694
T3 - Proceedings - 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021
SP - 237
EP - 239
BT - Proceedings - 2021 IEEE International Symposium on Performance Analysis of Systems and Software, ISPASS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 March 2021 through 30 March 2021
ER -