Cloud-Blockchain Integrated Privacy-Preserving Distributed Computation Offloading Scheme in the Internet of Vehicles
DOI:
https://doi.org/10.4108/eetsis.14026Keywords:
internet of vehicles, secure computation offloading, blockchain, privacy preservation, distributed computingAbstract
INTRODUCTION: With the rapid development of the Internet of Vehicles technology, on-board applications in intelligent vehicles are increasingly facing the challenge of processing latency-sensitive and computationally intensive tasks. Although vehicle-mounted computing units possess certain computing capabilities, their limited resources cannot cope with complex latency-sensitive tasks. Offloading computations to resource-rich cloud servers is a feasible solution to this problem. However, transmission costs and communication delays on remote cloud servers become major bottlenecks for offloading latency-sensitive tasks. The emergence of Mobile Edge Computing (MEC) shifts computing power from centralized cloud servers to the network edge, reducing latency. Nevertheless, existing schemes still suffer from poor fault tolerance, privacy leakage, and low computational efficiency.
OBJECTIVES: To address these limitations, this paper proposes a cloud-blockchain integrated privacy-preserving distributed computation offloading scheme (FCOS).
METHODS: FCOS significantly improves fault tolerance via a redundant distributed computing mechanism, effectively mitigating the impact of single-point failures on computations. RESULTS: Smart contracts on the blockchain guarantee verifiability and fairness throughout the computation offloading process, enhancing overall offloading security. For privacy protection, the scheme combines homomorphic encryption and blind factor technology to protect data privacy with low computational overhead on intelligent vehicle terminals.
CONCLUSION: Compared with existing methods, FCOS reduces the average computation time by 90.4% for polynomial calculations within 5000 iterations in the cloud computing phase. Under a single-point failure rate ranging from 0% to 60%, the average computation time for polynomial calculations within 10,000 iterations is reduced by 92.9% to 96.7%.
References
[1] Ahmed M, Raza S, Mirza MA, Aziz A, Khan MA, Khan WU, Li J, Han Z. A survey on vehicular task offloading: classification, issues, and challenges. J King Saud Univ Comput Inf Sci. 2022;34(7):4135-4162.
[2] Raza S, Wang S, Ahmed M, Anwar MR. A survey on vehicular edge computing: architecture, applications, technical issues, and future directions. Wirel Commun Mob Comput. 2019;2019:3159762.
[3] Li S, Xie Y, Shi M, Zheng X, Lu Y. Mobile edge computing empowered energy consumption optimization for multiuser power IoT networks. EAI Endorsed Trans Scalable Inf Syst. 2025;12(3). doi: 10.4108/eetsis.8678.
[4] Guan Y, Zheng H, Shao J, Lu R, Wei G. Fair outsourcing polynomial computation based on the blockchain. IEEE Trans Serv Comput. 2022;15(5):2795-2808.
[5] Sun PJ. Security and privacy protection in cloud computing: discussions and challenges. J Netw Comput Appl. 2020;160:102642.
[6] Yu X, Yan Z, Vasilakos AV. A survey of verifiable computation. Mob Netw Appl. 2017;22(3):438-453.
[7] Abbes S, Rekhis S. A blockchain-based solution for reputation management in IoV. In: Proceedings of the 2021 International Wireless Communications and Mobile Computing Conference (IWCMC); 28 June–2 July 2021; Harbin City, China. Piscataway, NJ: IEEE; 2021. p. 1129-1134.
[8] Rehman M, Khan ZA, Javed MU, Iftikhar MZ, Majeed U, Bux I, Javaid N. A blockchain-based distributed vehicular network architecture for smart cities. In: Proceedings of the Workshops of the 34th International Conference on Advanced Information Networking and Applications; 15–17 April 2020; Caserta, Italy. Cham: Springer; 2020. p. 320-331.
[9] Xiao T, Chen C, Pei Q, Song HH. Consortium blockchain-based computation offloading using mobile edge platoon cloud in Internet of Vehicles. IEEE Trans Intell Transp Syst. 2022;23(10):17769-17783.
[10] Javaid U, Sikdar B. A secure and scalable framework for blockchain-based edge computation offloading in social Internet of Vehicles. IEEE Trans Veh Technol. 2021;70(5):4022-4036.
[11] Narayan G, Haveri P, Rashmi B, Deewan Y. A framework for data provenance assurance in cloud environment using Ethereum blockchain. EAI Endorsed Trans Scalable Inf Syst. 2023;11(2). doi: 10.4108/eetsis.3536.
[12] Bhurani K, Dogra A, Agarwal P, Shrivastava P, Singh TP, Bhandwal M. Smart contracts for ensuring data integrity in cloud storage with blockchain. EAI Endorsed Trans Scalable Inf Syst. 2024;11(6). doi: 10.4108/eetsis.5633.
[13] Khan MMA, Sarwar HMA, Awais M. Gas consumption analysis of Ethereum blockchain transactions. Concurr Comput Pract Exp. 2022;34(4):e6679.
[14] Ozdemir A, Wahby R, Whitehat B, Boneh D. Scaling verifiable computation using efficient set accumulators. In: Proceedings of the 29th USENIX Security Symposium; 12–14 August 2020; Virtual Event. Berkeley, CA: USENIX Association; 2020. p. 2075-2092.
[15] Dorsala MR, Sastry VN, Chapram S. Fair payments for verifiable cloud services using smart contracts. Comput Secur. 2020;90:101712.
[16] Yao Y, Chen W, Chen X, Ding J, Pan S. A blockchain-based privacy-preserving scheme for vehicular trust management systems. In: Proceedings of the 2020 International Conference on Internet of Things and Intelligent Applications (ITIA); 27–29 November 2020; Zhenjiang, China. Piscataway, NJ: IEEE; 2020. p. 1-5.
[17] Xue J, Yan W. Edge computing communication privacy protection method based on federated learning algorithm. EAI Endorsed Trans Scalable Inf Syst. 2026;12(9).
[18] Song M, Sang Y, Zeng Y, Luo S. Blockchain-based secure outsourcing of polynomial multiplication and its application in fully homomorphic encryption. Secur Commun Netw. 2021;2021:9962575.
[19] Cordi C, Frank MP, Gabert K, Helinski C, Kao RC, Kolesnikov V, Ladha A, Pattengale N. Auditable, available and resilient private computation on the blockchain via MPC. In: Cyber Security, Cryptology, and Machine Learning (CSCML 2022), LNCS 13301; 30 June–1 July 2022; Be'er Sheva, Israel. Cham: Springer; 2022. p. 281-299.
[20] Kumari P, Kaur P. A survey of fault tolerance in cloud computing. J King Saud Univ Comput Inf Sci. 2021;33(10):1159-1176.
[21] Malik MK, Goel V, Swaroop A. A new hybrid COA-OOA based task scheduling and fuzzy logic approach to increase fault tolerance in cloud computing. EAI Endorsed Trans Scalable Inf Syst. 2024;11(6).
[22] Gennaro R, Gentry C, Parno B. Non-interactive verifiable computing: outsourcing computation to untrusted workers. In: Advances in Cryptology - CRYPTO 2010, LNCS 6223. 15–19 August 2010; Santa Barbara, CA, USA. Berlin, Heidelberg: Springer; 2010. p. 465-482.
[23] Ahmad H, Wang L, Hong H, Li J, Dawood H, Ahmed M, Yang Y. Primitives towards verifiable computation: a survey. Front Comput Sci. 2018;12(3):451-478.
[24] Fiore D, Gennaro R. Publicly verifiable delegation of large polynomials and matrix computations, with applications. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security; 16–18 October 2012; Raleigh, NC, USA. New York, NY: ACM; 2012. p. 501-512.
[25] Sun J, Zhu B, Qin J, Hu J, Ma J. Confidentiality-preserving publicly verifiable computation schemes for polynomial evaluation and matrix-vector multiplication. Secur Commun Netw. 2018;2018:5275132.
[26] Liu J, Bi J, Li M. Secure outsourcing of large matrix determinant computation. Front Comput Sci. 2020;14(6):146807.
[27] Li Y, Ghosh D, Gupta P, Mehrotra S, Panwar N, Sharma S. Prism: private verifiable set computation over multi-owner outsourced databases. In: Proceedings of the 2021 International Conference on Management of Data; 20–25 June 2021; Virtual Event, China. New York, NY: ACM; 2021. p. 1116-1128.
[28] Zhang Y, Deng RH, Liu X, Zheng D. Blockchain-based efficient and robust fair payment for outsourcing services in cloud computing. Inf Sci. 2018;462:262-277.
[29] Campanelli M, Gennaro R, Goldfeder S, Nizzardo L. Zero-knowledge contingent payments revisited: attacks and payments for services. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security; 30 October–3 November 2017; Dallas, TX, USA. New York, NY: ACM; 2017. p. 229-243.
[30] Dong C, Wang Y, Aldweesh A, McCorry P, Van Moorsel A. Betrayal, distrust, and rationality: smart counter-collusion contracts for verifiable cloud computing. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security; 30 October–3 November 2017; Dallas, TX, USA. New York, NY: ACM; 2017. p. 211-227.
[31] Huang H, Chen X, Wu Q, Huang X, Shen J. Bitcoin-based fair payments for outsourcing computations of fog devices. Future Gener Comput Syst. 2018;78(2):850-858.
[32] Vos J, Conti M, Erkin Z. Efficient batchable secure outsourced computation: depth-aware arithmetization of common primitives for BFV & BGV. In: Proceedings of the 34th USENIX Security Symposium (USENIX Security 25); 13–15 August 2025; Seattle, WA, USA. Berkeley, CA: USENIX Association; 2025. p. 8385-8402.
[33] Dong M, Zhang C, Bai Y, Chen Y. Efficient multi-party private set union without non-collusion assumptions. In: Proceedings of the 34th USENIX Security Symposium (USENIX Security 25); 13–15 August 2025; Seattle, WA, USA. Berkeley, CA: USENIX Association; 2025. p. 2005-2024.
[34] Xie Y, Wu Q, Fan P, Cheng N, Chen W, Wang J, Letaief KB. Resource allocation for twin maintenance and task processing in vehicular edge computing network. IEEE Internet Things J. 2025;12(15):32008-32021.
[35] Marcolla C, Sucasas V, Manzano M, Bassoli R, Fitzek FH, Aaraj N. Survey on fully homomorphic encryption, theory, and applications. Proc IEEE. 2022;110(10):1572-1609.
[36] Zhang J, Cheng X, Yang L, Hu J, Liu X, Chen K. SoK: fully homomorphic encryption accelerators. ACM Comput Surv. 2024;56(12):316.
[37] Mishra PK, Rathee D, Duong DH, Yasuda M. Fast secure matrix multiplications over ring-based homomorphic encryption. Inf Secur J Glob Perspect. 2021;30(4):219-234.
[38] Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Advances in Cryptology - EUROCRYPT'99, LNCS 1592. 2–6 May 1999; Prague, Czech Republic. Berlin, Heidelberg: Springer; 1999. p. 223-238.
[39] Song B, Zhou D, Wu J, Yuan X, Zhu Y, Wang C. Protecting function privacy and input privacy in the publicly verifiable outsourcing computation of polynomial functions. Future Internet. 2023;15(4):152.
[40] ETSI. Multi-access edge computing (MEC); use cases and requirements. ETSI GS MEC 002 V3.2.1.; 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Huayong Wei
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
