Data Integrity Verification Scheme For Lightweight Devices in Cloud Storage Scenarios

Han Bing; Wang Hao; Fang Min; Zhang Yongchao; Zhou Lu; Ge Chunpeng

doi:10.7544/issn1000-1239.202440489

Journal of Computer Research and Development > 2024 > 61(10): 2467-2481. > DOI: 10.7544/issn1000-1239.202440489 CSTR: 32373.14.issn1000-1239.202440489

Han Bing, Wang Hao, Fang Min, Zhang Yongchao, Zhou Lu, Ge Chunpeng. Data Integrity Verification Scheme For Lightweight Devices in Cloud Storage Scenarios[J]. Journal of Computer Research and Development, 2024, 61(10): 2467-2481. DOI: 10.7544/issn1000-1239.202440489

Citation:

PDF (1842 KB)

Data Integrity Verification Scheme For Lightweight Devices in Cloud Storage Scenarios

1.
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106
2.
School of Data Science and Engineering, East China Normal University, Shanghai 200062
3.
School of Cyberspace Security, Southeast University, Nanjing 211189
4.
School of Software, Shandong University, Jinan 250101
5.
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Software School, Shandong University, Jinan 250101

Funds: This work was supported by the National Key Research and Development Program of China (2021YFB2700503), the National Natural Science Foundation of China (62071222, 62032025, U21A20467, U20A20176, U22B2030), the Natural Science Foundation of Jiangsu Province (BK20220075), and the Shenzhen Science and Technology Program (JCYJ20210324134810028).

More Information

Author Bio:
Han Bing: born in 2000. Master. Her main research interests include trusted execution environment and data security

Wang Hao: born in 1996. PhD. His main research interests include blockchain and privacy-preserving

Fang Min: born in 1996. PhD. Her main research interests include blockchain data management, trusted hardware, and privacy-preserving computing

Zhang Yongchao: born in 1994. PhD. His main research interests include network traffic measurement, graph stream analysis, and network security

Zhou Lu: born in 1990. PhD, professor. Her main research interests include blockchain, cryptographic and security solutions for the Internet of things

Ge Chunpeng: born in 1987. PhD, professor. His main research interests include information security and privacy-preserving for cloud computing, blockchain, and security and privacy of AI systems
Received Date: May 30, 2024
Revised Date: July 17, 2024
Available Online: September 13, 2024

Graphical Abstract

Abstract

Abstract

Lightweight mobile devices with limited resources often alleviate their computational and storage burdens by outsourcing large-scale data to cloud storage servers. However, this cloud storage model is susceptible to the possibility of selfish cloud servers discarding data to conserve storage resources. Therefore, there is a need for effective integrity verification of cloud-stored data to ensure its correct and intact storage. Existing cloud storage integrity verification mechanisms lack a reliable approach to perform real-time, multiple verifications of data under the premise of data privacy protection. We propose an integrity verification mechanism based on a trusted execution environment. It generates trustworthy proofs in isolated areas to ensure that the cloud server remains unaware of the data and the entire proof generation process, thereby compelling honest assurance of data integrity throughout the process. To further enhance the security of the proposed solution, we introduce blockchain smart contracts to provide trustworthy storage and verification of proofs. Additionally, we address the issue of resource scarcity on the client side by proposing an efficient verification mechanism based on cuckoo filters. Experimental results demonstrate that this method can achieve high execution efficiency and practicality while ensuring the integrity verification of private data.
- integrity verification,
- cloud storage,
- trusted execution environment,
- blockchain,
- cuckoo filter,
- privacy-preserving

FullText(HTML)

References (16)

References

[1]	Li Jiaxing, Wu Jigang, Jiang Guiyuan, et al. Blockchain-based public auditing for big data in cloud storage[J/OL]. Information Processing & Management, 2020[2024-07-16]. https://doi.org/10.1016/j.ipm.2020.102382
[2]	Garg N, Nehra A, Baza M, et al. Secure and efficient data integrity verification scheme for cloud data storage[C]//Proc of the 20th IEEE Consumer Communications & Networking Conf. Piscataway, NJ: IEEE, 2023: 1−6
[3]	Fan Yongkai, Lin Xiaodong, Tan Gang, et al. One secure data integrity verification scheme for cloud storage[J]. Future Generation Computer Systems, 2019, 96: 376−385 doi: 10.1016/j.future.2019.01.054
[4]	Zhao Quanyu, Chen Siyi, Liu Zheli, et al. Blockchain-based privacy-preserving remote data integrity checking scheme for IoT information systems[J]. Information Processing & Management, 2020, 57(6): 102355
[5]	Babitha M, Babu K. Secure cloud storage using aes encryption[C]//Proc of the Int Conf on Automatic Control and Dynamic Optimization Techniques. Piscataway, NJ: IEEE, 2016: 859−864
[6]	Seth B, Dalal S, Le D, et al. Secure cloud data storage system using hybrid Paillier–Blowfish algorithm[J/OL]. Computers, Materials & Continua, 2021[2024-07-16]. https://doi.org/10.32604/cmc.2021.014466
[7]	Sarkar M, Kumar S. Ensuring data storage security in cloud computing based on hybrid encryption schemes[C]//Proc of the 4th Int Conf on Parallel Distributed and Grid Computing. Piscataway, NJ: IEEE, 2016: 320−325
[8]	Morales-Sandoval M, Cabello M, Marin-Castro H, et al. Attribute-based encryption approach for storage, sharing and retrieval of encrypted data in the cloud[J]. IEEE Access, 2020(8): 170101−170116
[9]	Yu Yong, Au M, Ateniese G, et al. Identity-based remote data integrity checking with perfect data privacy preserving for cloud storage[J]. IEEE Transactions on Information Forensics and Security, 2016, 12(4): 767−778
[10]	Ping Yuan, Zhan Yu, Lu Ke, et al. Public data integrity verification scheme for secure cloud storage[J]. Information, 2020, 11(9): 409. https://doi.org/10.3390/info11090409
[11]	Zhu Hongliang, Yuan Ying, Chen Yuling, et al. A secure and efficient data integrity verification scheme for cloud-IoT based on short signature[J]. IEEE Access, 2019(7): 90036−90044
[12]	Garg N, Bawa S, Kumar N. An efficient data integrity auditing protocol for cloud computing[J]. Future Generation Computer Systems, 2020(109): 306−316
[13]	Fan Yongkai, Lin Xiaodong, Liang Wei, et al. A secure privacy preserving deduplication scheme for cloud computing[J]. Future Generation Computer Systems, 2019(101): 127−135
[14]	Kurnikov A, Paverd A, Mannan M, et al. Keys in the clouds: Auditable multi-device access to cryptographic credentials[C]//Proc of the 13th Int Conf on Availability, Reliability and Security. New York: ACM , 2018: 1−10
[15]	王惠峰,李战怀,张晓,等. 云存储中数据完整性自适应审计方法[J]. 计算机研究与发展,2017,54(1):172−183 doi: 10.7544/issn1000-1239.2017.20150900 Wang Huifeng, Li Zhanhuai, Zhang Xiao, et cl. A self-adaptive audit method of data integrity in the cloud storage[J]. Journal of Computer Research and Development, 2017, 54(1): 172−183 (in Chinese) doi: 10.7544/issn1000-1239.2017.20150900
[16]	Fan Bin, Andersen D, Kaminsky M, et al. Cuckoo filter: Practically better than Bloom[C]//Proc of the 10th ACM Int on Conf on emerging Networking Experiments and Technologies. New York: ACM , 2014: 75−88

[1]	Cao Yiran, Zhu Youwen, He Xingyu, Zhang Yue. Utility-Optimized Local Differential Privacy Set-Valued Data Frequency Estimation Mechanism[J]. Journal of Computer Research and Development, 2022, 59(10): 2261-2274. DOI: 10.7544/issn1000-1239.20220504
[2]	Hong Jinxin, Wu Yingjie, Cai Jianping, Sun Lan. Differentially Private High-Dimensional Binary Data Publication via Attribute Segmentation[J]. Journal of Computer Research and Development, 2022, 59(1): 182-196. DOI: 10.7544/issn1000-1239.20200701
[3]	Wu Wanqing, Zhao Yongxin, Wang Qiao, Di Chaofan. A Safe Storage and Release Method of Trajectory Data Satisfying Differential Privacy[J]. Journal of Computer Research and Development, 2021, 58(11): 2430-2443. DOI: 10.7544/issn1000-1239.2021.20210589
[4]	Zhang Yuxuan, Wei Jianghong, Li Ji, Liu Wenfen, Hu Xuexian. Graph Degree Histogram Publication Method with Node-Differential Privacy[J]. Journal of Computer Research and Development, 2019, 56(3): 508-520. DOI: 10.7544/issn1000-1239.2019.20170886
[5]	Zhu Weijun, You Qingguang, Yang Weidong, Zhou Qinglei. Trajectory Privacy Preserving Based on Statistical Differential Privacy[J]. Journal of Computer Research and Development, 2017, 54(12): 2825-2832. DOI: 10.7544/issn1000-1239.2017.20160647
[6]	Wu Yingjie, Zhang Liqun, Kang Jian, Wang Yilei. An Algorithm for Differential Privacy Streaming Data Adaptive Publication[J]. Journal of Computer Research and Development, 2017, 54(12): 2805-2817. DOI: 10.7544/issn1000-1239.2017.20160555
[7]	Wang Liang, Wang Weiping, Meng Dan. Privacy Preserving Data Publishing via Weighted Bayesian Networks[J]. Journal of Computer Research and Development, 2016, 53(10): 2343-2353. DOI: 10.7544/issn1000-1239.2016.20160465
[8]	Lu Guoqing, Zhang Xiaojian, Ding Liping, Li Yanfeng, Liao Xin. Frequent Sequential Pattern Mining under Differential Privacy[J]. Journal of Computer Research and Development, 2015, 52(12): 2789-2801. DOI: 10.7544/issn1000-1239.2015.20140516
[9]	Ouyang Jia, Yin Jian, Liu Shaopeng, Liu Yubao. An Effective Differential Privacy Transaction Data Publication Strategy[J]. Journal of Computer Research and Development, 2014, 51(10): 2195-2205. DOI: 10.7544/issn1000-1239.2014.20130824
[10]	Ni Weiwei, Chen Geng, Chong Zhihong, Wu Yingjie. Privacy-Preserving Data Publication for Clustering[J]. Journal of Computer Research and Development, 2012, 49(5): 1095-1104.

Cited By

Cited by

Periodical cited type(5)

1.	张涵，于航，周继威，白云开，赵路坦. 面向隐私计算的可信执行环境综述. 计算机应用. 2025(02): 467-481 .
2.	付裕，林璟锵，冯登国. 虚拟化与密码技术应用:现状与未来. 密码学报(中英文). 2024(01): 3-21 .
3.	徐传康，李忠月，刘天宇，种统洪，杨发雪. 基于可信执行环境的汽车域控系统安全研究. 汽车实用技术. 2024(15): 18-25+73 .
4.	徐文嘉，岑孟杰，陈亮. 隐私保护下单细胞RNA测序数据细胞分类研究. 医学信息学杂志. 2024(10): 86-89 .
5.	孙钰，熊高剑，刘潇，李燕. 基于可信执行环境的安全推理研究进展. 信息网络安全. 2024(12): 1799-1818 .