DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning

doi:10.1007/s11227-022-04745-4

	DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning
	Liu, Zizhen 1; Chen, Si 2; Ye, Jing 1,3; Fan, Junfeng 2; Li, Huawei 1,3; Li, Xiaowei 1,3
	2022-08-24
发表期刊	JOURNAL OF SUPERCOMPUTING
ISSN	0920-8542
页码	31
摘要	Secure aggregation is widely used in horizontal federated learning (FL), to prevent the leakage of training data when model updates from data owners are aggregated. Secure aggregation protocols based on homomorphic encryption (HE) have been utilized in industrial cross-silo FL systems, one of the settings involved with privacy-sensitive organizations such as financial or medical, presenting more stringent requirements on privacy security. However, existing HE-based solutions have limitations in efficiency and security guarantees against colluding adversaries without a Trust Third Party. This paper proposes an efficient Doubly Homomorphic Secure Aggregation (DHSA) scheme for cross-silo FL, which utilizes multi-key homomorphic encryption (MKHE) and seed homomorphic pseudorandom generator (SHPRG) as cryptographic primitives. The application of MKHE provides strong security guarantees against up to N - 2 participates colluding with the aggregator, with no TTP required. To mitigate the large computation and communication cost of MKHE, we leverage the homomorphic property of SHPRG to replace the majority of MKHE computation by computationally friendly mask generation from SHPRG, while preserving the security. Overall, the resulting scheme satisfies the stringent security requirements of typical cross-silo FL scenarios, at the same time providing high computation and communication efficiency for practical usage. We experimentally demonstrate that our scheme brings a speedup to 20x over the state-of-the-art HE-based secure aggregation and reduces the traffic volume to approximately 1.5x inflation over the plain learning setting.
关键词	Federated learning Security Efficient Homomorphic
DOI	10.1007/s11227-022-04745-4
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2020YFB1600201] ; National Natural Science Foundation of China (NSFC)[U20A20202] ; National Natural Science Foundation of China (NSFC)[62090024] ; National Natural Science Foundation of China (NSFC)[61876173] ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000843969200001
出版者	SPRINGER
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19435
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ye, Jing
作者单位	1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China 2.Open Secur Res, 18 Sci & Technol Rd, Shenzhen 518063, Peoples R China 3.CASTEST, 18 Zhongguancun Rd, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Liu, Zizhen,Chen, Si,Ye, Jing,et al. DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning[J]. JOURNAL OF SUPERCOMPUTING,2022:31.
APA	Liu, Zizhen,Chen, Si,Ye, Jing,Fan, Junfeng,Li, Huawei,&Li, Xiaowei.(2022).DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning.JOURNAL OF SUPERCOMPUTING,31.
MLA	Liu, Zizhen,et al."DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning".JOURNAL OF SUPERCOMPUTING (2022):31.