隐私计算在车路协同场景应用的探索与实践

李明; 吕阿斌

doi:10.11959/j.issn.2096-0271.2022069

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隐私计算在车路协同场景应用的探索与实践

专题：数据流通与隐私计算 | 更新时间：2024-06-03

- 隐私计算在车路协同场景应用的探索与实践
- Exploration and practice of privacy preserving computing for vehicle-road collaboration system
- 大数据 2022年8卷第5期页码：74-87
- 作者机构：
- 作者简介：
  
  [ "李明（1978- ），男，中兴飞流信息科技有限公司副总经理，大数据系统计算技术国家工程实验室副主任，主要研究方向为大数据、人工智能、云存储、分布式数据库等" ]
  [ "吕阿斌（1970- ）男，中兴飞流信息科技有限公司董事长、总经理，主要研究方向为云计算、大数据、人工智能等" ]
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022069
  中图分类号： U49
- 网络首发：2022-09，
  
  纸质出版：2022-09-15
- 稿件说明：
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李明, 吕阿斌. 隐私计算在车路协同场景应用的探索与实践[J]. 大数据, 2022,8(5):74-87.

Ming LI, Abin LYU. Exploration and practice of privacy preserving computing for vehicle-road collaboration system[J]. Big data research, 2022, 8(5): 74-87.
李明, 吕阿斌. 隐私计算在车路协同场景应用的探索与实践[J]. 大数据, 2022,8(5):74-87. DOI： 10.11959/j.issn.2096-0271.2022069.

Ming LI, Abin LYU. Exploration and practice of privacy preserving computing for vehicle-road collaboration system[J]. Big data research, 2022, 8(5): 74-87. DOI： 10.11959/j.issn.2096-0271.2022069.

摘要

基于车路协同的发展现状，总结车路协同场景中隐私计算、人工智能等技术的研究进展。设计并实现YITATFL平台，为数据管理、模型训练、模型管理及协同推理提供完备的隐私保护方案，为人工智能结合隐私计算在交通行业的应用提供参考。

Abstract

Based on the development of vehicle-road collaboration

the research progress of privacy computing

artificial intelligence

and other technologies for the vehicle-road collaboration scene was summarized.YITA-TFL platform was designed and implemented.A complete privacy protection scheme was provided for data management

model training

model management

and collaborative reasoning.And a model was established for the application of artificial intelligence combined with privacy computing in the transportation industry.

关键词

Keywords

references

陈超 , 吕植勇 , 付姗姗 , 等 . 国内外车路协同系统发展现状综述 [J ] . 交通信息与安全 , 2011 , 29 ( 1 ): 102 - 105 , 109 .

CHEN C , LYU Z Y , FU S S , et al . Overview of the development in cooperative vehicle-infrastructure system home and abroad [J ] . Journal of Transport Information and Safety , 2011 , 29 ( 1 ): 102105 ,109.

李克强 , 戴一凡 , 李升波 , 等 . 智能网联汽车(ICV)技术的发展现状及趋势 [J ] . 汽车安全与节能学报 , 2017 , 8 ( 1 ): 1 - 14 .

LI K Q , DAI Y F , LI S B , et al . State-ofthe-art and technical trends of intelligent and connected vehicles [J ] . Journal of Automotive Safety and Energy , 2017 , 8 ( 1 ): 1 - 14 .

国务院 . “十四五”现代综合交通运输体系发展规划 [Z ] . 2021 .

The State Council . Development plan of modern comprehensive transportation system during the “14th Five-Year Plan” [Z ] . 2021 .

交通运输部 . 数字交通“十四五”发展规划 [Z ] . 2021 .

Ministry of Transport of the People’s Republic of China . Development plan of digital transportation during the “14th Five-Year Plan” [Z ] . 2021 .

马承恩 . 车路协同相关产业发展趋势 [J ] . 电子产品世界 , 2021 , 28 ( 9 ): 4 - 6 , 106 .

MA C E . The development trend of vehicle-infrastructure cooperation industry [J ] . Electronic Engineering ＆Product World , 2021 , 28 ( 9 ): 4 - 6 , 106 .

GU T Y , DOLAN-GAVITT B , GARG S . BadNets:identifying vulnerabilities in the machine learning model supply chain [J ] . arXiv preprint,2017,arXiv:1708.06733 .

MOHAMMED N , CHEN R , FUNG B C M , et al . Differentially private data release for data mining [C ] // Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2011 : 493 - 501 .

傅继彬 , 张啸剑 , 丁丽萍 . MAXGDDP:基于差分隐私的决策数据发布算法 [J ] . 通信学报 , 2018 , 39 ( 3 ): 136 - 146 .

FU J B , ZHANG X J , DING L P . MAXGDDP:decision data release with differential privacy [J ] . Journal on Communications , 2018 , 39 ( 3 ): 136 - 146 .

ZHANG J , CORMODE G , PROCOPIUC C M , et al . PrivBayes [J ] . ACM Transactions on Database Systems , 2017 , 42 ( 4 ): 1 - 41 .

王瑞锦 , 唐榆程 , 张巍琦 , 等 . 基于同态加密和区块链技术的车联网隐私保护方案 [J ] . 网络与信息安全学报 , 2020 , 6 ( 1 ): 46 - 53 .

WANG R J , TANG Y C , ZHANG W Q , et al . Privacy protection scheme for Internet of vehicles based on homomorphic encryption and block chain technology [J ] . Chinese Journal of Network and Information Security , 2020 , 6 ( 1 ): 46 - 53 .

GEYER R C , KLEIN T , NABI M . Differentially private federated learning:a client level perspective [J ] . arXiv preprint,2017,arXiv:1712.07557 .

ABADI M , CHU A , GOODFELLOW I , et al . Deep learning with differential privacy [C ] // Proceedings of 2016 ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2016 : 308 - 318 .

MAO Y L , HONG W B , WANG H , et al . Privacy-preserving computation offloading for parallel deep neural networks training [J ] . IEEE Transactions on Parallel and Distributed Systems , 2021 , 32 ( 7 ): 1777 - 1788 .

THAPA C , CHAMIKARA M A P , CAMTEPE S , et al . SplitFed:when federated learning meets split learning [J ] . arXiv preprint,2020,arXiv:2004.12088 .

吴茂强 , 黄旭民 , 康嘉文 , 等 . 面向车路协同推断的差分隐私保护研究 [J ] . 计算机工程 , 2022 , 48 ( 7 ): 29 - 35 .

WU M Q , HUANG X M , KANG J W , et al . Research on differential privacy protection for collaborative vehicle-road inference [J ] . Computer Engineering , 2022 , 48 ( 7 ): 29 - 35 .

中国信息通信研究院 . 车联网白皮书 [Z ] . 2021 .

China Academy of lnformation and Communications Technology . White paper on Internet of vehicles [Z ] . 2021 .

中国智能交通产业联盟 . 车路协同信息交互技术要求 [Z ] . 2021 .

China ITS Industry Alliance . Technical requirements for vehicleroad coordination system information interaction [Z ] . 2021 .

袁博 , 王思源 . 隐私计算产品评估体系 [J ] . 信息通信技术与政策 , 2021 , 47 ( 6 ): 12 - 18 .

YUAN B , WANG S Y . Privacy preserving computing product evaluation system [J ] . Information and Communications Technology and Policy , 2021 , 47 ( 6 ): 12 - 18 .

MCMAHAN B , MOORE E , RAMAGe D , et al . Communication-efficient learning of deep networks from decentralized data [C ] // Proceedings of the 20th International Conference on Artificial Intelligence and Statistics .[S.l.:s.n. ] , 2017 : 1273 - 1282 .

梁天恺 , 曾碧 , 陈光 . 联邦学习综述：概念、技术、应用与挑战 [J ] . 计算机应用 , 2021 :已录用.

LIANG T K , ZENG B , CHEN G . Federated learning survey:concept,technology,application and challenge [J ] . Journal of Computer Applications , 2021 :accepted.

DWORK C . Differential privacy [M ] // Encyclopedia of cryptography and security . Boston : Springer US , 2011 : 338 - 340 .

谭作文 , 张连福 . 机器学习隐私保护研究综述 [J ] . 软件学报 , 2020 , 31 ( 7 ): 2127 - 2156 .

TAN Z W , ZHANG L F . Survey on privacy preserving techniques for machine learning [J ] . Journal of Software , 2020 , 31 ( 7 ): 2127 - 2156 .

BHOWMICK A , DUCHI J , FREUDIGER J , et al . Protection against reconstruction and its applications in private federated learning [J ] . arXiv preprint,2018,arXiv:1812.00984 .

CARLINI N , LIU C , KOS J , et al . The secret sharer:measuring unintended neural network memorization ＆ extracting secrets [J ] . arXiv preprint,2018,arXiv:1802.08232 .

王健宗 , 孔令炜 , 黄章成 , 等 . 联邦学习隐私保护研究进展 [J ] . 大数据 , 2021 , 7 ( 3 ): 130 - 149 .

WANG J Z , KONG L W , HUANG Z C , et al . Research advances on privacy protection of federated learning [J ] . Big Data Research , 2021 , 7 ( 3 ): 130 - 149 .

SHOKRI R , SHMATIKOV V . Privacypreserving deep learning [C ] // Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . New York:ACM Press , 2015 : 1310 - 1321 .

UCHIDA Y , NAGAI Y , SAKAZAWA S , et al . Embedding watermarks into deep neural networks [C ] // Proceedings of 2017 ACM on International Conference on Multimedia Retrieval . New York:ACM Press , 2017 : 269 - 277 .

谢宸琪 , 张保稳 , 易平 . 人工智能模型水印研究综述 [J ] . 计算机科学 , 2021 , 48 ( 7 ): 9 - 16 .

XIE C Q , ZHANG B W , YI P . Survey on artificial intelligence model watermarking [J ] . Computer Science , 2021 , 48 ( 7 ): 9 - 16 .

樊雪峰 , 周晓谊 , 朱冰冰 , 等 . 深度神经网络模型版权保护方案综述 [J ] . 计算机研究与发展 , 2022 , 59 ( 5 ): 953 - 977 .

FAN X F , ZHOU X Y , ZHU B B , et al . Survey of copyright protection schemes based on DNN model [J ] . Journal of Computer Research and Development , 2022 , 59 ( 5 ): 953 - 977 .

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