Efficient and secure big data storage system with leakage resilience in cloud computing

Zhang, Yinghui; Yang, Menglei; Zheng, Dong; Lang, Pengzhen; Wu, Axin; Chen, Chen

doi:10.1007/s00500-018-3435-z

Cited by 30 publications

(12 citation statements)

References 53 publications

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“…The amount of leakage of private key is limited between two consecutive private key updates, while the whole leakage amount may be arbitrary large. Zhang et al [116] presented a continuous leakage-resilient identity-based encryption scheme (CLR-IBE) to protect data security from partial secret key leakage in the continuous leakage model. It is a big data storage system in cloud computing.…”

Section: Leakage-resilientmentioning

confidence: 99%

“…As long as the key is updated regularly and the key secret information is not leaked in the process, the continuous leakage elasticity can be obtained. This scheme has the same leakage ratio to [116].…”

Section: Leakage-resilientmentioning

confidence: 99%

“…Due to the diversity of cloud data, conventional privacy-preserving mechanisms are unable to provide comprehensive privacy protection in the cloud. Therefore, protection schemes [115], [116], [118] about sensitive information privacy, identity privacy and attributes privacy etc. are developed to achieve more specified privacy protection.…”

Section: E Privacy-preservingmentioning

confidence: 99%

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Data Security and Privacy Protection for Cloud Storage: A Survey

2020

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The new development trends including Internet of Things (IoT), smart city, enterprises digital transformation and world's digital economy are at the top of the tide. The continuous growth of data storage pressure drives the rapid development of the entire storage market on account of massive data generated. By providing data storage and management, cloud storage system becomes an indispensable part of the new era. Currently, the governments, enterprises and individual users are actively migrating their data to the cloud. Such a huge amount of data can create magnanimous wealth. However, this increases the possible risk, for instance, unauthorized access, data leakage, sensitive information disclosure and privacy disclosure. Although there are some studies on data security and privacy protection, there is still a lack of systematic surveys on the subject in cloud storage system. In this paper, we make a comprehensive review of the literatures on data security and privacy issues, data encryption technology, and applicable countermeasures in cloud storage system. Specifically, we first make an overview of cloud storage, including definition, classification, architecture and applications. Secondly, we give a detailed analysis on challenges and requirements of data security and privacy protection in cloud storage system. Thirdly, data encryption technologies and protection methods are summarized. Finally, we discuss several open research topics of data security for cloud storage.

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Section: Leakage-resilientmentioning

confidence: 99%

Section: Leakage-resilientmentioning

confidence: 99%

Section: E Privacy-preservingmentioning

confidence: 99%

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Data Security and Privacy Protection for Cloud Storage: A Survey

2020

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“…In recent years, cloud computing has developed rapidly with its advantages of ultra-large-scale storage, powerful computing power, high scalability, and low cost [ 1 ]. Any company or individual can access cloud computing servers through a payment mode [ 2 , 3 , 4 , 5 , 6 ]. At the same time, with the advancement of computer technology and the development of big data, artificial intelligence, and the Internet of Things (IoT), the demand for data interaction analysis for mass terminals has rapidly increased [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Zhang

Zhao

Zheng

et al. 2018

Sensors

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As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

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“…We know that cloud storage technology is the most common and most popular cloud computing service today. The extensive application of cloud storage motivates enterprises and organizations to outsource data storage to third-party cloud providers [12][13][14][15][16]. Zhang et al [17] proposed a fine-grained access control system suitable for resource-constrained users in cloud computing.…”

Section: Introductionmentioning

confidence: 99%

Secure Deduplication Based on Rabin Fingerprinting over Wireless Sensing Data in Cloud Computing

Zhang

Yang

et al. 2018

Security and Communication Networks

Self Cite

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The rapid advancements in the Internet of Things (IoT) and cloud computing technologies have significantly promoted the collection and sharing of various data. In order to reduce the communication cost and the storage overhead, it is necessary to exploit data deduplication mechanisms. However, existing data deduplication technologies still suffer security and efficiency drawbacks. In this paper, we propose two secure data deduplication schemes based on Rabin fingerprinting over wireless sensing data in cloud computing. The first scheme is based on deterministic tags and the other one adopts random tags. The proposed schemes realize data deduplication before the data is outsourced to the cloud storage server, and hence both the communication cost and the computation cost are reduced. In particular, variable-size block-level deduplication is enabled based on the technique of Rabin fingerprinting which generates data blocks based on the content of the data. Before outsourcing data to the cloud, users encrypt the data based on convergent encryption technologies, which protects the data from being accessed by unauthorized users. Our security analysis shows that the proposed schemes are secure against offline brute-force dictionary attacks. In addition, the random tag makes the second scheme more reliable. Extensive experimental results indicate that the proposed data deduplication schemes are efficient in terms of the deduplication rate, the system operation time, and the tag generation time.

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Efficient and secure big data storage system with leakage resilience in cloud computing

Cited by 30 publications

References 53 publications

Data Security and Privacy Protection for Cloud Storage: A Survey

Data Security and Privacy Protection for Cloud Storage: A Survey

Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Secure Deduplication Based on Rabin Fingerprinting over Wireless Sensing Data in Cloud Computing

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