Please wait a minute...
浙江大学学报(工学版)  2023, Vol. 57 Issue (1): 144-154    DOI: 10.3785/j.issn.1008-973X.2023.01.015
计算机技术、通信工程     
基于区块链的车联网矩阵计算安全卸载方案
刘雪娇1(),宋庆武1,夏莹杰2,*()
1. 杭州师范大学 信息科学与技术学院,浙江 杭州 311121
2. 浙江大学 计算机科学与技术学院,浙江 杭州 310027
Secure computation offloading scheme for matrix in Internet of vehicles based on blockchain
Xue-jiao LIU1(),Qing-wu SONG1,Ying-jie XIA2,*()
1. School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
 全文: PDF(1383 KB)   HTML
摘要:

为了保护任务数据的机密性,验证计算结果的正确性,提出基于区块链的车联网矩阵计算安全卸载方案. 该方案利用编写在区块链上的智能合约自动执行计算结果验证过程,保证验证过程的安全性和结果的不可篡改. 方案提出的基于矩阵加法的轻量级矩阵加密方法,简化了矩阵计算卸载加解密的复杂度. 通过与现有方案比较可知,本文方案能够在有效保护车辆的敏感信息的同时,实现对矩阵计算结果正确性的验证. 利用所构建的矩阵加密方法,能够有效地降低车辆侧矩阵加解密的计算开销.

关键词: 车辆边缘计算安全计算卸载区块链矩阵乘法车联网    
Abstract:

A secure computation offloading scheme for matrix in the Internet of Vehicle based on blockchain was proposed in order to protect the confidentiality of the task data and verify the correctness of the computation results. The smart contract was used based on blockchain to automatically execute the verification process of the computation results, which can ensure the security of the verification process and the tamper resistance of the verification result. A lightweight matrix encryption method was proposed based on matrix addition, which can simplify the complexity of encryption and decryption in matrix computation offloading. The proposed scheme can effectively protect the sensitive information of the vehicle and verify the correctness of the matrix computation results compared with the existing schemes. The computing overhead of encryption and decryption on the vehicle side can be effectively reduced by the matrix encryption method.

Key words: vehicular edge computing    secure computation offloading    blockchain    matrix multiplication    Internet of Vehicle
收稿日期: 2022-04-13 出版日期: 2023-01-17
CLC:  TP 301  
基金资助: 国家自然科学基金资助项目(61873232);浙江省自然科学基金资助项目(LY19F020021);2022年浙江省大学生科技创新活动计划(新苗人才计划)资助项目(2022R426B067)
通讯作者: 夏莹杰     E-mail: liuxuejiao0406@163.com;xiayingjie@zju.edu.cn
作者简介: 刘雪娇(1984—),女,副教授,从事车联网安全的研究. orcid.org/0000-0003-1821-2864. E-mail: liuxuejiao0406@163.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  
刘雪娇
宋庆武
夏莹杰

引用本文:

刘雪娇,宋庆武,夏莹杰. 基于区块链的车联网矩阵计算安全卸载方案[J]. 浙江大学学报(工学版), 2023, 57(1): 144-154.

Xue-jiao LIU,Qing-wu SONG,Ying-jie XIA. Secure computation offloading scheme for matrix in Internet of vehicles based on blockchain. Journal of ZheJiang University (Engineering Science), 2023, 57(1): 144-154.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.01.015        https://www.zjujournals.com/eng/CN/Y2023/V57/I1/144

图 1  矩阵计算卸载模型
图 2  矩阵计算卸载的流程
方法 加密时间 解密时间 验证方式 客户端验
证开销
文献[27]方法 $ \mathcal{{{O}}}\left( {{{m}}+2{{{m}}^2}} \right) $ $ \mathcal{{{O}}}\left( {{{ms}}} \right) $ 客户端 $ \mathcal{{{O}}}\left( {{{kms}}} \right) $
文献[28]方法 $\begin{gathered} \mathcal{O} (m+2n+s+ \\ 2 (mn+ns))\end{gathered}$ $\mathcal{{{O}}}\left( {{{mn}}+4{{ms}}+{{ns}}} \right)$ 客户端 $ \mathcal{{{O}}}\left( {{{kms}}} \right) $
文献[29]方法 $\begin{gathered} \mathcal{O} ( {m}^2+{n}^2+ {s}^2 + \\ 3( mn+ns) )\end{gathered}$ $\mathcal{{{O}}}\left( {{{mn}}+2{{ms}}+{{ns}}} \right)$ 客户端 $\mathcal{{{O}}}\left( {{{kms}}} \right)$
文献[10]方法 $\mathcal{{{O}}}\left( {{{s}}+{{ns}}} \right)$ $\mathcal{{{O}}}\left( {\dfrac{1}{2}{{mn}}+\dfrac{3}{2}{{ms}}} \right)$ 第三方
服务器
0
本文方法 $\mathcal{{{O}}}\left( {{{n}}+{{s}}+{{ns}}} \right)$ $\mathcal{{{O}}}\left( {{{mn}}+{{ms}}} \right)$ 智能合约 $ \mathcal{{{O}}}\left( 1 \right) $
表 1  与其他矩阵乘法计算卸载方案的比较
方法 公开可
验证
零元素
保护
抗合谋
攻击
抗抵赖
攻击
可追责性 Pv
文献[27]方法 × × × × ${1 /{{2^{{k}}}}}$
文献[28]方法 × × × ${1 /{{2^{{k}}}}}$
文献[29]方法 × × × ${1 /{{2^{{k}}}}}$
文献[10]方法 × × $ {1 / {{2^{n+m}}}} $
本文方法 $ {1/{{2^{2n+m}}}} $
表 2  与其他方案的安全性对比
图 3  矩阵加密计算开销(m ꞉ n ꞉ s = 4 ꞉ 5 ꞉ 6)
图 4  矩阵解密的计算开销(m ꞉ n ꞉ s = 4 ꞉ 5 ꞉ 6)
图 5  结果验证计算开销(m ꞉ n ꞉ s = 4 ꞉ 5 ꞉ 6)
图 6  与车辆本地计算开销的对比(m ꞉ n ꞉ s = 4 ꞉ 5 ꞉ 6)
1 李智勇, 王琦, 陈一凡, 等 车辆边缘计算环境下任务卸载研究综述[J]. 计算机学报, 2021, 44 (5): 963- 982
LI Zhi-yong, WANG Qi, CHEN Yi-fan, et al A survey on task offloading research in vehicular edge computing[J]. Chinese Journal of Computers, 2021, 44 (5): 963- 982
doi: 10.11897/SP.J.1016.2021.00963
2 DAI H, ZENG X Y, YU Z L, et al A scheduling algorithm for autonomous driving tasks on mobile edge computing servers[J]. Journal of Systems Architecture, 2019, 94: 14- 23
doi: 10.1016/j.sysarc.2019.02.004
3 HU L, TIAN Y W, YANG J, et al Ready player one: UAV-clustering-based multi-task offloading for vehicular VR/AR gaming[J]. IEEE Network, 2019, 33 (3): 42- 48
doi: 10.1109/MNET.2019.1800357
4 赵梓铭, 刘芳, 蔡志平, 等 边缘计算: 平台、应用与挑战[J]. 计算机研究与发展, 2018, 55 (2): 327- 337
ZHAO Zi-ming, LIU Fang, CAI Zhi-ping, et al Edge computing: platforms, applications and challenges[J]. Journal of Computer Research and Development, 2018, 55 (2): 327- 337
doi: 10.7544/issn1000-1239.2018.20170228
5 GENNARO R, GENTRY C, PARNO B. Non-interactive verifiable computing: outsourcing computation to untrusted workers [C]// Annual Cryptology Conference. Berlin: Springer, 2010: 465-482.
6 MISHRA P K, RATHEE D, DUONG D H, et al Fast secure matrix multiplications over ring-based homomorphic encryption[J]. Information Security Journal: A Global Perspective, 2020, 30 (4): 219- 234
7 JIANG X Q, KIM M, LAUTER K, et al. Secure outsourced matrix computation and application to neural networks [C]// Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2018: 1209-1222.
8 OLIVA G, CIOABA S, HADJICOSTIS C N Distributed calculation of edge-disjoint spanning trees for robustifying distributed algorithms against man-in-the-middle attacks[J]. IEEE Transactions on Control of Network Systems, 2017, 5 (4): 1646- 1656
9 ABBAS N, ZHANG Y, TAHERKORDI A, et al Mobile edge computing: a survey[J]. IEEE Internet of Things Journal, 2017, 5 (1): 450- 465
10 ZHANG S M, LI H W, DAI Y S, et al. EPP-DMM: an efficient and privacy-protected delegation scheme for matrix multiplication [C]// IEEE Global Communications Conference. Piscataway: IEEE, 2017: 1-6.
11 ZHANG S M, LI H W, DAI Y S, et al Verifiable outsourcing computation for matrix multiplication with improved efficiency and applicability[J]. IEEE Internet of Things Journal, 2018, 5 (6): 5076- 5088
doi: 10.1109/JIOT.2018.2867113
12 王晨旭, 程加成, 桑新欣, 等 区块链数据隐私保护: 研究现状与展望[J]. 计算机研究与发展, 2021, 58 (10): 2099- 2119
WANG Chen-xu, CHENG Jia-cheng, SANG Xin-xin, et al Data privacy-preserving for blockchain: state of the art and trends[J]. Journal of Computer Research and Development, 2021, 58 (10): 2099- 2119
doi: 10.7544/issn1000-1239.2021.20210804
13 MOLLAH M B, ZHAO J, NIYATO D, et al Blockchain for the internet of vehicles towards intelligent transportation systems: a survey[J]. IEEE Internet of Things Journal, 2020, 8 (6): 4157- 4185
14 俞建业, 戚湧, 王宝茁 基于Spark的车联网分布式组合深度学习入侵检测方法[J]. 计算机科学, 2021, 48 (Supple.1): 518- 523
YU Jian-ye, QI Yong, WANG Bao-zhuo Distributed combination deep learning intrusion detection method for Internet of vehicles based on spark[J]. Computer Science, 2021, 48 (Supple.1): 518- 523
doi: 10.11896/jsjkx.200700129
15 VASUDEVAN A, ANDERSON A, GREGG D. Parallel multi channel convolution using general matrix multiplication [C]// 28th IEEE International Conference on Application-Specific Systems, Architectures and Processors. Piscataway: IEEE, 2017: 19-24.
16 ZHOU J, WU F, ZHANG K, et al. Joint optimization of offloading and resource allocation in vehicular networks with mobile edge computing [C]// 10th International Conference on Wireless Communications and Signal Processing. Piscataway: IEEE, 2018: 1-6.
17 MOURAD A, TOUT H, WAHAB O A, et al Ad Hoc vehicular fog enabling cooperative low-latency intrusion detection[J]. IEEE Internet of Things Journal, 2020, 8 (2): 829- 843
18 CUI M Y, ZHONG S P, LI B Y, et al Offloading autonomous driving services via edge computing[J]. IEEE Internet of Things Journal, 2020, 7 (10): 10535- 10547
doi: 10.1109/JIOT.2020.3001218
19 WANG S M, YE D D, HUANG X M, et al Consortium blockchain for secure resource sharing in vehicular edge computing: a contract-based approach[J]. IEEE Transactions on Network Science and Engineering, 2020, 8 (2): 1189- 1201
20 LIAO H J, MU Y S, ZHOU Z Y, et al Blockchain and learning-based secure and intelligent task offloading for vehicular fog computing[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22 (7): 4051- 4063
doi: 10.1109/TITS.2020.3007770
21 ISLAM S, BADSHA S, SENGUPTA S, et al Blockchain-enabled intelligent vehicular edge computing[J]. IEEE Network, 2021, 35 (3): 125- 131
doi: 10.1109/MNET.011.2000554
22 AVIZHEH S, NABI M, SAFAVI R, et al. Verifiable computation using smart contracts [C]// Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop. New York: ACM, 2019: 17-28.
23 DORSALA M R, SASTRY V N, CHAPRAM S Fair payments for verifiable cloud services using smart contracts[J]. Computers and Security, 2020, 90: 101712
doi: 10.1016/j.cose.2019.101712
24 DONG C Y, WANG Y L, ALDWEESH A, et al. Betrayal, distrust, and rationality: smart counter-collusion contracts for verifiable cloud computing [C]// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York: ACM, 2017: 211-227.
25 SALINAS S, LUO C Q, CHEN X H, et al. Efficient secure outsourcing of large-scale linear systems of equations [C]// IEEE Conference on Computer Communications. Piscataway: IEEE, 2015: 1035-1043.
26 ZHANG X Y, JIANG T, LI K C, et al New publicly verifiable computation for batch matrix multiplication[J]. Information Sciences, 2019, 479: 664- 678
doi: 10.1016/j.ins.2017.11.063
27 KONG S, CAI Y, XUE F, et al Cloud outsourcing computing security protocol of matrix multiplication computation based on similarity transformation[J]. International Journal of Wireless and Mobile Computing, 2018, 14 (1): 90- 96
doi: 10.1504/IJWMC.2018.089984
28 FU S J, YU Y P, XU M. A secure algorithm for outsourcing matrix multiplication computation in the cloud [C]// Proceedings of the 5th ACM International Workshop on Security in Cloud Computing. New York: ACM, 2017: 27-33.
29 WU Y, LIAO Y J, LIANG Y K, et al Secure and efficient protocol for outsourcing large-scale matrix multiplication to the cloud[J]. IEEE Access, 2020, 8: 227556- 227565
doi: 10.1109/ACCESS.2020.3045999
[1] 刘雪娇,王慧敏,夏莹杰,赵思苇. 具有隐私保护的车联网空间众包任务分配方法[J]. 浙江大学学报(工学版), 2022, 56(7): 1267-1275.
[2] 张海波,刘子琪,刘开健,徐勇军. 活跃度感知的社交车辆分簇算法[J]. 浙江大学学报(工学版), 2022, 56(5): 1044-1054.
[3] 何苗,柏粉花,于卓,沈韬. 区块链中可公开验证密钥共享技术[J]. 浙江大学学报(工学版), 2022, 56(2): 306-312.
[4] 董思含,信俊昌,郝琨,姚钟铭,陈金义. 多区块链环境下的连接查询优化算法[J]. 浙江大学学报(工学版), 2022, 56(2): 313-321.
[5] 孙亮,李晓风,赵赫,余斌,周桐,李皙茹. 基于NFT的实物上链资产化方法[J]. 浙江大学学报(工学版), 2022, 56(10): 1900-1911.
[6] 梁秀波,吴俊涵,赵昱,尹可挺. 区块链数据安全管理和隐私保护技术研究综述[J]. 浙江大学学报(工学版), 2022, 56(1): 1-15.
[7] 刘雪娇,殷一丹,陈蔚,夏莹杰,许佳丽,韩立东. 基于区块链的车联网数据安全共享方案[J]. 浙江大学学报(工学版), 2021, 55(5): 957-965.
[8] 陈蔚,刘雪娇,夏莹杰. 基于层次分析法的车联网多因素信誉评价模型[J]. 浙江大学学报(工学版), 2020, 54(4): 722-731.
[9] 盛念祖, 李芳, 李晓风, 赵赫, 周桐. 基于区块链智能合约的物联网数据资产化方法[J]. 浙江大学学报(工学版), 2018, 52(11): 2150-2158.
[10] 田翔 周凡 陈耀武 刘莉 陈耀. 基于FPGA的实时双精度浮点矩阵乘法器设计[J]. J4, 2008, 42(9): 1611-1615.