Data duplicates. The duplicate copies of identical file eliminate by file level 2. For the block level duplication which The notion of proof of ownership PoW [11] enables users to eliminates duplicates blocks of data that occur in non-identical prove their ownership of data copies to the storage server.
Proof: The user can demonstrate his identity to a verifier by Characteristic: performing some identification proof related to his identity. To identify the attacks that exploit client-side deduplication.. Verify: The verifier occurs verification with input of public information. Proofs of ownership provide the rigorous security. Rigorous efficiency requirements of Peta-byte scale 3. Following are the different methods which are used in secure data deduplication in cloud storage.
Although convergent one copy of each file uploaded. Message lock encryption is encryption has been extensively acquired for secure used to resolve the problem of clients encrypt their file deduplication, a uncertain issue of making convergent however the saving are lock.
Dupless is used to provide encryption practical is to efficiently and reliably manage a secure deduplicated storage as well as storage resisting brute- huge number of convergent keys. Clients encrypt under message-based keys obtained from a key-server via an oblivious PRF protocol in Techniques: dupless server. It allow clients to store encrypted data with an 1. Key management existing service, have the service occurs deduplication on their on the part , and yet achieves strong confidentiality 2.
Convergent Encryption[4] guarantees. It show that encryption for deduplicated storage can successfully reach desired performance and space savings 3.
Bugiel, S. Nurnberger, A. Sadeghi, and T. Schneider Characteristic: proposed architecture for secure outsourcing of data and 1. More Security. In come towards, the user communicates with a trusted cloud.
Easily-deployed solution for encryption that supports Which encrypts as well as verifies the data stored and deduplication operations occurred in the untrusted cloud. It divide the 3. User Friendly: Use command-line client that supports computations such that the trusted cloud is used for security- both Dropbox and Google Drive.
Resolve the problem of message lock Encryption. Client-side Most important issue in the cloud storage is utilization of the deduplication tries to identify deduplication chance already at storage capacity.
In this paper, there are two categories of the client and save the bandwidth of uploading copies of data deduplication strategy, and extend the fault-tolerant existing files to the server[11]. To overcome the attacks Shai digital signature scheme proposed by Zhang on examining Halevi1, Danny Harnik, Benny Pinkas, and Alexandra redundancy of blocks to achieve the data deduplication. The Shulman-Peleg proposes the Proof of ownership which lets a proposed scheme in this paper not only reduces the cloud client efficiently prove to a server that that the client keep a storage capacity, but also improves the speed of data file, rather than just some short information about it present deduplication.
Furthermore, the signature is computed for every uploaded file for verifying the integrity of files. Bellare, S. Keelveedhi, and T. Halevi, D. Harnik, B. Li, X. Chen, M. Li, J. Lee, and W. Lou deduplication with multiple server. Ng, Y. It servers and to manage its database servers. It also provides provides high performance as well as resolves the virtual infrastructure to host application services. These cross user duplication.
In addition, small overhead compared to naive client-side the web interface is used by the users to retrieve, modify and deduplication. It identifies attacks and saving restore data from the cloud, depending on their access rights. Convergent key share across 5. Cloud as a proxy that provides a clearly defined interface to manage the outsourced data, programs, [3] J.
Yuan and S. Secure and constant cost public cloud and queries. It having low latency and also provide storage auditing with deduplication. IACR Cryptology the secure execution environment. Li, P. Secure storage Enhance the efficiency of data as well as deduplication with efficient and reliable convergent Improve speed of data duplication. Message- the Help of token generation and Secure upload download it locked encryption and secure deduplication.
Security analysis determine that given [6] J. Xu, E. Chang and J. Weak leakage-resilient schemes are secure in terms of insider as well as outsider client-side deduplication of encrypted data in cloud attacks specified in the proposed security model.
As a proof of storage. In the recommended system, three types of keys have to be generated. The eq. This encrypted file contains two CTs, and mathematically, they are depicted as,. When the same user tries to upload the same file again, the CS calculates the hash value with the CK by utilizing the SHA algorithm.
Next, for every single input file, the binary depiction of the file is split into fixed-sized blocks. The size of the data block finds the level of granularity of deduplication. As the data block size decreases, the level of deduplication increases. Meanwhile, it might bring complex metadata management. Then, the tag key is created for each of the divided blocks. Next, the hash value is computed for all the tag keys utilizing the same SHA algorithm. In the uploading phase, the CS checks the hashtag HT for a particular input file.
If the hashtag value of the input file is in that HT, then the CS queries the path of the hash tree to the users. If a user sends the correct path, then the CS verifies the user id. If the id is the same, then the CS does not store the file again. Generally, the hash tree path has the succeeding format,. The same user trying to upload the same file is mathematically denoted as,. Where, S u denotes the same user, S f represents the same file, and CS means the cloud server, which informs the file is previously available A.
When different users try to upload the same files to the CS, the file is split into several blocks, and a tag is created for checking the duplicate data copies in CSP. Then, each tag is converted into HC, and it is called a hashtag value. The CS checks the HT for the input file grounded on the hashtag value. If the hashtag value is available in the HT, then the CS asks the path of the hash tree of the input file. Different users trying to upload the same file to the CS is expressed as,. Where D u denotes the different users.
R l is the reference link and I u denotes an invalid user. Here, the user sends the tag value of the specified file. The CS now checks the hashtag value, whether it is in the HT. If the value is available, then the CS lets the user download the file, else the CS considers them as an invalid user.
It is mathematically denoted as,. Where H T denotes the hashtag value. Pseudocode for the proposed secure deduplication system is evinced below,.
The implemented deduplication methodology is deployed in the JAVA programming environment with the following system configuration. In this section, the performance scrutiny is done on the proposed system. E t is considered as the time that an encryption algorithm consumes to generate encrypted data as of the inputted data.
Encryption time is computed as the difference between the encryption ending time and encryption starting time. It is evaluated as,.
Where E t is the encryption time, E e is the encryption ending time and E s is the encryption starting time. D t is defined as the difference between the decryption ending time and decryption starting time. Security is highly essential for cloud storage. The security level is computed by dividing the hacked data with the number of the original text.
The security level of the system is expressed as,. The comparison is performed, centered on the uploaded file size. The E t and D t are denoted in seconds s. So, it is inferred that the suggested MECC algorithm takes less E t and D t when contrasted to the remaining techniques. Tables 1 and 2 are graphically plotted and are displayed in Fig. The E t and D t time varies centered on the file sizes. Here, the file sizes range from 5mb to 25mb. So, it is deduced that the MECC attains the best performance when contrasted to others.
Table 3 compared the performance rendered by the proposed MECC technique with the prevailing methods concerning the key generation time and security level.
Here, the existing Diffie-Hellman DH method takes more time for key generation. But the proposed MECC takes less time to generate a key when contrasted to other techniques. Furthermore, the security level of the proposed and existing methods is compared with existing techniques. So, it is inferred that the proposed MECC proffers high performance for both key generation and security.
Table 2 is graphically illustrated as displayed in Fig. Here, the proposed deduplication scheme is contrasted to other techniques concerning the deduplication rate and tree generation time.
Performance analysis of the proposed deduplication scheme with existing techniques in terms of deduplication rate. The deduplication rate varies centered on the file size. Similarly, for other file sizes such as 10mb, 15mb, and 20mb, the proposed deduplication scheme gives superior results contrasted to CRT and SDM. The performance comparison of the proposed hash tree used in deduplication with the existing binary tree in respect of tree generation time is evinced in Fig.
Figure 6 contrasts the performance shown by the proposed hash tree with the existing binary tree regarding tree generation time. So it is deduced that the proposed hash tree approach shows high-level performance compared to binary tree generation methodology.
Deduplication is the utmost notable Data compression methodology. Many existing methods introduced different deduplication methods, but they provided low security. This paper proposed a secure deduplication system using convergent and MECC algorithms over the cloud-fog environment.
The proposed method is analyzed in four ways: a when new users try to upload the new file, b when the same user tries to upload the same file, c when different users try to upload the same file, and d when different users try to download the file. The assessment result elucidates that the recommended system is extremely secure and effective for data deduplication for an integrated cloud environment. This proposed model may be extended in the future for any kind of Internet of Things IoT applications that use dynamic resources management at the edge environment.
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J Netw Comput Appl — Int J Eng Adv Technol — Cybern Inf Technol — Alazab M Profiling and classifying the behavior of malicious codes. Download references. You can also search for this author in PubMed Google Scholar. All authors have participated in the design of the proposed method and practical implementation. All authors have read and approved the manuscript. Correspondence to Mahdi Abbasi. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and Permissions. A secure data deduplication system for integrated cloud-edge networks. J Cloud Comp 9, 61 Download citation. Received : 23 March Accepted : 05 November Published : 19 November Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Menon 2 , Venu P. Abstract Data redundancy is a significant issue that wastes plenty of storage space in the cloud-fog storage integrated environments.
Introduction The data gathered through different sources and the Emergence of the Internet of Things in all aspects of applications increases data volume from petabytes to yottabytes, necessitating cloud computing paradigm and fog networks to process and store the data. The significant contributions of this paper can be summarized as follows. Related works The secure deduplication system abandons the duplicate copies of data, and it also proffers security to the data.
Full size image. Result and discussions The implemented deduplication methodology is deployed in the JAVA programming environment with the following system configuration. Performance analysis of proposed encryption technique Encryption time E t is considered as the time that an encryption algorithm consumes to generate encrypted data as of the inputted data. Performance comparison of the proposed hash tree with existing binary tree.
Conclusion Deduplication is the utmost notable Data compression methodology. Availability of data and materials Not applicable. References 1. Int J Eng Technol 7
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