DHSA: efficient doubly homomorphic secure aggregation for cross-silo federated learning
文献类型：期刊论文
| 作者 | Liu, Zizhen1; Chen, Si3; Ye, Jing1,2; Fan, Junfeng3; Li, Huawei1,2; Li, Xiaowei1,2 |
| 刊名 | JOURNAL OF SUPERCOMPUTING
 |
| 出版日期 | 2022-08-24 |
| 页码 | 31 |
| 关键词 | Federated learning Security Efficient Homomorphic |
| ISSN号 | 0920-8542 |
| DOI | 10.1007/s11227-022-04745-4 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000843969200001 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19434]  |
| 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.CASTEST, 18 Zhongguancun Rd, Beijing 100083, Peoples R China 3.Open Secur Res, 18 Sci & Technol Rd, Shenzhen 518063, 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. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。