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Journal articles on the topic 'Deduplication management'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Janka, Heidrun, and Maria-Inti Metzendorf. "High precision but variable recall – comparing the performance of five deduplication tools." Journal of EAHIL 20, no. 1 (2024): 12–17. http://dx.doi.org/10.32384/jeahil20607.

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Deduplication methods for multiple database searches conducted for evidence syntheses differ in terms of time invested, accuracy, and comprehensiveness of identified duplicates. Deduplication tools can significantly contribute to a more efficient conduct of the search task in evidence syntheses. Widely-used tools for deduplication include reference management software (e.g. EndNote), built-in deduplication features in systematic review software (e.g. Covidence, Rayyan), and automated deduplication tools (e.g. Deduklick, SRA Deduplicator). Newer tools leverage machine learning algorithms crafte

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2

P. Selvi. "Revolutionary Data Deduplication with Fuzzy C-Means: Advancing Data Quality Management." Communications on Applied Nonlinear Analysis 31, no. 6s (2024): 582–91. http://dx.doi.org/10.52783/cana.v31.1245.

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Maintaining the integrity and precision of data depends on the crucial process of data deduplication, the search and elimination of duplicate data from a database. Conventional deduplication methods may not be useful when dealing with data with variances and uncertainty as from time to time depend on spotting the closest matches. In this point, another procedure frequently applied in data clustering but especially for data deduplication is employed in this work to propose a new approach to data deduplication involving Fuzzy C-use (FCM) clustering. FCM allows to set as many data points as desir

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Patil, Janhavi Rahul. "DATA FINDING, SHARING AND DUPLICATION REMOVAL IN THE CLOUD." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29587.

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Deduplication involves eliminating duplicate or redundant data to reduce stored data volume, commonly used in data backup, network optimization, and storage management. However, traditional deduplication methods have limitations with encrypted data and security. The primary objective of this project is to develop new distributed deduplication systems that offer increased reliability. In these systems, data chunks are distributed across the Hadoop Distributed File System (HDFS), and a robust key management system is utilized to ensure secure deduplication with slave nodes. Instead of having mul

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Narwade, Aditya Rajesh. "CLOUD BASED DUPLICATION REMOVAL SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29104.

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Deduplication involves eliminating duplicate or redundant data to reduce stored data volume, commonly used in data backup, network optimization, and storage management. However, traditional deduplication methods have limitations with encrypted data and security. The primary objective of this project is to develop new distributed deduplication systems that offer increased reliability. In these systems, data chunks are distributed across the Hadoop Distributed File System (HDFS), and a robust key management system is utilized to ensure secure deduplication with slave nodes. Instead of having mul

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Lee, Mira, and Minhye Seo. "Secure and Efficient Deduplication for Cloud Storage with Dynamic Ownership Management." Applied Sciences 13, no. 24 (2023): 13270. http://dx.doi.org/10.3390/app132413270.

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Cloud storage services have become indispensable in resolving the constraints of local storage and ensuring data accessibility from anywhere at any time. Data deduplication technology is utilized to decrease storage space and bandwidth requirements. This technology has the potential to save up to 90% of space by eliminating redundant data in cloud storage. The secure data sharing in cloud (SeDaSC) protocol is an efficient data-sharing solution supporting secure deduplication. In the SeDaSC protocol, a cryptographic server (CS) encrypts clients’ data on behalf of clients to reduce their computa

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Gnana Jeslin, J., and P. Mohan Kumar. "Decentralized and Privacy Sensitive Data De-Duplication Framework for Convenient Big Data Management in Cloud Backup Systems." Symmetry 14, no. 7 (2022): 1392. http://dx.doi.org/10.3390/sym14071392.

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The number of customers transferring information to cloud storage has grown significantly, with the rising prevalence of cloud computing. The rapidly rising data volume in the cloud, mostly on one side, is followed by a large replication of data. On the other hand, if there is a single duplicate copy of stored symmetrical information in the de-duplicate cloud backup the manipulation or lack of a single copy may cause untold failure. Thus, the deduplication of files and the auditing of credibility are extremely necessary and how they are achieved safely and effectively must be addressed in acad

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Gan, Chuang, Weichun Wang, Yuchong Hu, et al. "Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication." Applied Sciences 15, no. 3 (2025): 1245. https://doi.org/10.3390/app15031245.

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Outsourcing storage to the cloud can save storage costs and is commonly used in businesses. It should fulfill two major goals: storage efficiency and data confidentiality. Encrypted deduplication can achieve both goals via performing deduplication to eliminate the duplicate data within encrypted data. Traditional encrypted deduplication generates the encryption key on the client side, which poses a risk of offline brute-force cracking of the outsourced data. Server-aided encryption schemes have been proposed to strengthen the confidentiality of encrypted deduplication by distributing the encry

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8

Zhao, Lihong, and Weiping Zhong. "Design and optimization of financial management information system in colleges and universities under the background of big data." Journal of Computational Methods in Sciences and Engineering 25, no. 2 (2024): 1906–22. https://doi.org/10.1177/14727978241307146.

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In order to promote the effect of financial management (FM) informatization in colleges and universities (CAU), this paper combines the big data technology to design the FM informatization system in CAU and conducts a detailed study on the BF algorithm. Aiming at the problem of misjudgment in the process of URL deduplication, this paper proposes a BF algorithm based on Hash splitting with multiple eigenvalues and analyzes and compares its performance with experiments. Finally, the effectiveness of the algorithm in reducing the misjudgment rate of deduplication is confirmed. Then, this paper pr

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9

Youn, Taek-Young, Nam-Su Jho, Keonwoo Kim, Ku-Young Chang, and Ki-Woong Park. "Locked Deduplication of Encrypted Data to Counter Identification Attacks in Cloud Storage Platforms." Energies 13, no. 11 (2020): 2742. http://dx.doi.org/10.3390/en13112742.

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Deduplication of encrypted data is a significant function for both the privacy of stored data and efficient storage management. Several deduplication techniques have been designed to provide improved security or efficiency. In this study, we focus on the client-side deduplication technique, which has more advantages than the server-side deduplication technique, particularly in communication overhead, owing to conditional data transmissions. From a security perspective, poison, dictionary, and identification attacks are considered as threats against client-side deduplication. Unfortunately, in

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BALGOTRA, ANSH. "Data Duplication Detection and Removal System Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46920.

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Abstract— The problem of missing data is a critical issue in various domains, as it can lead to inaccurate analysis and flawed decision-making. Traditional methods for handling missing values have been replaced by machine learning techniques, which offer more efficient solutions. Research in this area has explored various approaches to data imputation, analyzing their strengths and limitations. A systematic literature review of studies from 2016 to 2021 identified key factors influencing the effectiveness of thesemethods, providing valuable insights for researchers and data analysts. In parall

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Abo Hussein, Safa Ali, R. Badlishah Ahmad, Naimah Yaakob, Fathey Mohammed, and Abdul Ghani Khan. "Content-Defined Chunking Algorithms in Data Deduplication: Performance, Trade-Offs and Future-Oriented Techniques." Journal of Advanced Research in Applied Sciences and Engineering Technology 52, no. 1 (2024): 21–34. http://dx.doi.org/10.37934/araset.52.1.2134.

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In the digital era, the exponential growth of data presents significant challenges for storage efficiency and processing speed. This paper reviews Content-Defined Chunking (CDC), a cornerstone in data deduplication technology, aimed at addressing these challenges. We systematically examine various CDC algorithms, categorising them into hashing-based and hash-less methodologies, and evaluating their performance in deduplication processes. Through a critical analysis of existing literature, the study identifies the balance between chunking speed and deduplication efficacy as a pivotal area for e

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S. Seethalakshmi and Dr. B. Balakumar. "Efficient and Secure Data Deduplication in Distributed Cloud Storage: A Fault-Tolerant Model Using Active Learning for Big Data Management." International Journal of Enhanced Research in Science, Technology & Engineering 13, no. 10 (2024): 05–12. http://dx.doi.org/10.55948/ijerste.2024.0102.

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As the big data era advances, more and more redundant data are being expressed in various ways. Data deduplication knowledge has never been more important than it is now for lowering redundant data storage and enhancing data quality. Connecting several data tables and identifying distinct entries that point to the same item is typically required, particularly when multi-source data deduplication is involved. Active learning minimises the amount of data that needs to be annotated and trains the classical by choosing the data pieces with the greatest evidence divergence. This approach offers spe

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A., Mohamed Divan Masood, and K. Muthusundar S. "Cryptographic Hashing Method using for Secure and Similarity Detection in Distributed Cloud Data." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 107–10. https://doi.org/10.11591/ijeecs.v9.i1.pp107-110.

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The explosive increase of data brings new challenges to the data storage and supervision in cloud settings. These data typically have to be processed in an appropriate fashion in the cloud. Thus, any improved latency may origin animmense loss to the enterprises. Duplication detection plays a very main role in data management. Data deduplication calculates an exclusive fingerprint for each data chunk by using hash algorithms such as MD5 and SHA-1. The designed fingerprint is then comparing against other accessible chunks in a database that dedicates for storing the chunks. As an outcome, Dedupl

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Mohamed Divan Masood, A., and S. K. Muthusundar. "Cryptographic Hashing Method using for Secure and Similarity Detection in Distributed Cloud Data." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 1 (2018): 107. http://dx.doi.org/10.11591/ijeecs.v9.i1.pp107-110.

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<p>The explosive increase of data brings new challenges to the data storage and supervision in cloud settings. These data typically have to be processed in an appropriate fashion in the cloud. Thus, any improved latency may originanimmense loss to the enterprises. Duplication detection plays a very main role in data management. Data deduplication calculates an exclusive fingerprint for each data chunk by using hash algorithms such as MD5 and SHA-1. The designed fingerprint is then comparing against other accessible chunks in a database that dedicates for storing the chunks. As an outcome

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Archana, K., and Mrs Shubangini Patil. "Secure Data Deduplication with Dynamic Ownership Management in Cloud Storage." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 2273–77. http://dx.doi.org/10.31142/ijtsrd14486.

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Nandhini, R. "Deduplication of Data Storage Management in Cloud Computing." Research Journal of Engineering and Technology 10, no. 1 (2019): 46. http://dx.doi.org/10.5958/2321-581x.2019.00009.6.

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17

Yan, Zheng, Mingjun Wang, Yuxiang Li, and Athanasios V. Vasilakos. "Encrypted Data Management with Deduplication in Cloud Computing." IEEE Cloud Computing 3, no. 2 (2016): 28–35. http://dx.doi.org/10.1109/mcc.2016.29.

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18

Lakshmi, Prasanna, and al. et. "Secure Approach to Textual Data Deduplication in Cloud Systems: A Process of Design." International Journal of Computational Learning & Intelligence 4, no. 4 (2025): 799–808. https://doi.org/10.5281/zenodo.15464489.

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The exponential growth of textual data, particularly in Vision-and-Language Navigation (VLN) applications, poses significant challenges for efficient storage and management in cloud-based environments. While data deduplication is a vital technique for minimizing storage requirements, it often introduces critical security concerns. This paper proposes a novel deduplication framework aimed at enhancing storage efficiency without compromising data security. By integrating deduplication processes on both the client and cloud sides, the proposed system effectively reduces data redundancy while safe

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19

Handijono, Ardijan, and Zaldy Suhatman. "Meningkatkan Deduplikasi Data melalui Kesamaan Teks dalam Pembelajaran Mesin: Pendekatan Komprehensif." AKADEMIK: Jurnal Mahasiswa Humanis 4, no. 2 (2024): 602–15. http://dx.doi.org/10.37481/jmh.v4i2.955.

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The issue of dirty data, particularly duplicate data, is a common problem in data management that can affect data quality, operational efficiency, and decision-making. This study highlights the importance of implementing sustainable deduplication strategies as a key step in managing dirty data. We explore solutions for detecting duplicate data by measuring text similarity indices. In this study, the authors utilize a literature review research method. Through this method, we collected various journals on data deduplication and text similarity techniques, comparing several methods to identify t

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20

Queen, Sharon Ignatious. "A DATA BACKUP TECHNIQUE IN CLOUD COMPUTING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 4 (2019): 95–98. https://doi.org/10.5281/zenodo.2631167.

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In cloud storage efficiency and data integrity are the two important requirements. This benefits in sparing efforts on heavy data maintenance, management. Single copy of each file is stored in cloud even it is owned by number of users. By reducing reliability deduplication system improves the storage utilization. In this paper we study the problem of integrity auditing and secure deduplication on cloud data and to recovering the files in case of the file deletion or if the cloud gets destroyed due to any reason. To achieve both data integrity and deduplication in cloud, presenting two  se

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Venkatesh Babu, S., P. Ramya, and Jeffin Gracewell. "Content Deduplication with Granularity Tweak Based on Base and Deviation for Large Text Dataset." Scientific Programming 2022 (November 22, 2022): 1–17. http://dx.doi.org/10.1155/2022/9515181.

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The concept of storage optimization has evolved as one of the hottest research projects in big data which brings out better solutions such as data compression which almost converges towards the deduplication technique. Deduplication is a technique that finds and eliminates duplicate content by storing only the unique copies of data whose efficiency is being qualified based on the amount of duplicate content that they hideout from the data source. The deduplication technique is a well-established storage optimization technique, so in the due course of time, various tweaks have been provided for

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22

Viji, D., and Dr S. Revathy. "Comparative Analysis for Content Defined Chunking Algorithms in Data Deduplication." Webology 18, Special Issue 02 (2021): 255–68. http://dx.doi.org/10.14704/web/v18si02/web18070.

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Data deduplication works on eliminating redundant data and reducing storage consumption. Nowadays more data generated and it was stored in the cloud repeatedly, due to this large volume of storage will be consumed. Data deduplication tries to reduce data volumes disk space and network bandwidth can be to reduce costs and energy consumption for running storage systems. In the data deduplication method, data broken into small size of chunk or block. Hash ID will be calculated for all the blocks then it’s compared with existing blocks for duplication. Blocks may be fixed or variable size, compare

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Neeli, Sethu Sesha Synam. "Economical Approaches to NetApp Storage for Effective Database Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–6. https://doi.org/10.55041/ijsrem18719.

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Database storage is paramount in mission-critical applications, significantly influencing system performance metrics. In the contemporary technological landscape, the financial burden of procuring servers equipped with high- capacity storage solutions is considerable. To mitigate these expenses, organizations should evaluate alternative avenues, such as NetApp, a remarkably economical option. NetApp has many features, including data cloning, deduplication, and advanced snapshot management, which optimize the storage architecture where databases are housed. This discourse will delve into best p

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K, Archana, and Shubangini Patil Mrs. "Secure Data Deduplication with Dynamic Ownership Management in Cloud Storage." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2273–77. https://doi.org/10.31142/ijtsrd14486.

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In this paper, we propose a novel server side deduplication scheme for encrypted data. It allows the cloud server to control access to outsourced data even when the ownership changes dynamically by exploiting randomized convergent encryption and secure ownership group key distribution. This prevents data leakage not only to revoked users even though they previously owned that data, but also to an honest but curious cloud storage server. In addition, the proposed scheme guarantees data integrity against any tag inconsistency attack. Thus, security is enhanced in the proposed scheme. The efficie

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K, Sabeerath, and Manikandasaran S. Sundaram. "ESPoW: Efficient and secured proof of ownership method to enable authentic deduplicated data access in public cloud storage." Scientific Temper 15, no. 04 (2024): 3165–72. https://doi.org/10.58414/scientifictemper.2024.15.4.25.

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The exponential growth of data in cloud environments necessitates efficient storage management solutions. Data deduplication, a technique that eliminates redundant data, has emerged as a key strategy to optimize storage utilization and reduce costs. However, deduplication introduces security challenges, particularly in verifying data ownership and protecting against unauthorized access. This paper presents efficient and secured proof of ownership (ESPoW), a novel proof-verifier technique designed to authenticate data ownership in deduplicated cloud storage environments. ESPoW utilizes a challe

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Yan, Zheng, Lifang Zhang, Wenxiu Ding, and Qinghua Zheng. "Heterogeneous Data Storage Management with Deduplication in Cloud Computing." IEEE Transactions on Big Data 5, no. 3 (2019): 393–407. http://dx.doi.org/10.1109/tbdata.2017.2701352.

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Li, Jin, Xiaofeng Chen, Mingqiang Li, Jingwei Li, Patrick P. C. Lee, and Wenjing Lou. "Secure Deduplication with Efficient and Reliable Convergent Key Management." IEEE Transactions on Parallel and Distributed Systems 25, no. 6 (2014): 1615–25. http://dx.doi.org/10.1109/tpds.2013.284.

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Li, Mei, Hongjun Zhang, Yanjun Wu, and Chen Zhao. "Prefetch-aware fingerprint cache management for data deduplication systems." Frontiers of Computer Science 13, no. 3 (2018): 500–515. http://dx.doi.org/10.1007/s11704-017-7119-0.

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Amaechi, Marcel Chibuzor, Matthias Daniel, and Bennett E O. "Data Storage Management in Cloud Computing Using Deduplication Technique." International Journal of Computer Science and Engineering 7, no. 7 (2020): 1–7. http://dx.doi.org/10.14445/23488387/ijcse-v7i7p101.

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DP, Mishra, Amitha P, Reddy KS, Jami N, and Prasad MD. "Encrypted Data Management with Deduplication in Cloud Compu-ting." European Journal of Advances in Engineering and Technology 5, no. 1 (2018): 43–49. https://doi.org/10.5281/zenodo.10701833.

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<strong>ABSTRACT </strong> Cloud Computing is an information technology concept which plays a vital role in the data processing and data storing. It also plays a crucial role in the Internet of Things (IoT). The data stored in the cloud should be secured to prevent the unauthorized access. There comes a data security concept known as Encryption. In order to maintain the users Privacy and the security of the data is stored in the cloud in the encrypted or cipher-text format. By this, only the encrypted data is going to be stored in the cloud which reduces the usage of the storage devices up to

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Godavari, Amdewar, and Chapram Sudhakar. "A survey on deduplication systems." International Journal of Grid and Utility Computing 15, no. 2 (2024): 143–59. http://dx.doi.org/10.1504/ijguc.2024.137902.

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KUMAR, Mr M. A. R., and Mrs SRILATHA PULI. "FINDING DATA DEDUPLICATION USING CLOUD." YMER Digital 21, no. 05 (2022): 136–42. http://dx.doi.org/10.37896/ymer21.05/17.

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Data grows at the emotional rate of 50% per time, and 75% of the digital world is a copy1 Although keeping multiple clones of data is necessary to guarantee their availability and high continuity and the quantum of data redundancy is inordinate. By keeping a single dupe of repeated data, data deduplication is one of the most promising results to reduce the storage costs, and improve users experience by saving network bandwidth and reducing provisory time. However, this result must now solve many security issues to be fully satisfying. In this project we target the attacks from malicious client

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K, Sabeerath, and Manikandasaran S. Sundaram. "BTEDD: Block-level tokens for efficient data deduplication in public cloud infrastructures." Scientific Temper 15, no. 03 (2024): 2507–14. http://dx.doi.org/10.58414/scientifictemper.2024.15.3.16.

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In today's digital era, the exponential growth of data necessitates effective storage and management solutions. The cloud has vast storage possibilities to store huge amounts of data. Public access to the cloud leads to duplicate copies of data stored in the storage. Maintaining a single copy of data in the cloud is most important for efficient data storage management. This paper introduces a groundbreaking strategy for improving the efficacy of cloud storage through innovative data deduplication techniques at the block levels. The block-level duplication verification efficiently identifies th

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Karthik, Kumar S. "SECURE AUDITING AND DEDUPLICATION OF DATA ON CLOUD." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 289–92. https://doi.org/10.5281/zenodo.51419.

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With the development of cloud computing technology over the past decade, data outsourcing to the cloud storage service is an attractive trend; spare no effort in favor of Mass data maintenance and management. However, since the Outsourcing cloud storage is not entirely trustworthy, it raises on how to achieve deduplication cloud security technology while achieving a complete audit.   In this work, we study the problem of duplication of data on cloud and develop a method to achieve data deduplication. This paper gives a solution for storing the data on cloud without duplicate copies and al

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Pratibha, Miss Dive. "Secure Data-Deduplication with Dynamic Ownership Management in Cloud Storage." International Journal for Research in Applied Science and Engineering Technology V, no. III (2017): 603–5. http://dx.doi.org/10.22214/ijraset.2017.3112.

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Hur, Junbeom, Dongyoung Koo, Youngjoo Shin, and Kyungtae Kang. "Secure Data Deduplication with Dynamic Ownership Management in Cloud Storage." IEEE Transactions on Knowledge and Data Engineering 28, no. 11 (2016): 3113–25. http://dx.doi.org/10.1109/tkde.2016.2580139.

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Yuan, Haoran, Xiaofeng Chen, Tao Jiang, Xiaoyu Zhang, Zheng Yan, and Yang Xiang. "DedupDUM: Secure and scalable data deduplication with dynamic user management." Information Sciences 456 (August 2018): 159–73. http://dx.doi.org/10.1016/j.ins.2018.05.024.

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Panse, Fabian, Maurice van Keulen, and Norbert Ritter. "Indeterministic Handling of Uncertain Decisions in Deduplication." Journal of Data and Information Quality 4, no. 2 (2013): 1–25. http://dx.doi.org/10.1145/2435221.2435225.

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Manghi, Paolo, Claudio Atzori, Michele De Bonis, and Alessia Bardi. "Entity deduplication in big data graphs for scholarly communication." Data Technologies and Applications 54, no. 4 (2020): 409–35. http://dx.doi.org/10.1108/dta-09-2019-0163.

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PurposeSeveral online services offer functionalities to access information from “big research graphs” (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although dedu

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Singhal, Shubhanshi, Akanksha Kaushik, and Pooja Sharma. "A Novel approach of data deduplication for distributed storage." International Journal of Engineering & Technology 7, no. 2.4 (2018): 46. http://dx.doi.org/10.14419/ijet.v7i2.4.10040.

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Due to drastic growth of digital data, data deduplication has become a standard component of modern backup systems. It reduces data redundancy, saves storage space, and simplifies the management of data chunks. This process is performed in three steps: chunking, fingerprinting, and indexing of fingerprints. In chunking, data files are divided into the chunks and the chunk boundary is decided by the value of the divisor. For each chunk, a unique identifying value is computed using a hash signature (i.e. MD-5, SHA-1, SHA-256), known as fingerprint. At last, these fingerprints are stored in the i

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Mohanaprakash, Thottipalayam Andavan, and Vairaperumal Nirmalrani. "Privacy protection domain-user integra tag deduplication in cloud data server." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 4155–63. https://doi.org/10.11591/ijece.v12i4.pp4155-4163.

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The cloud with strong storage management has recently developed in the big data world which can confirm the data integrity and keep just a single data duplicate. Many cloud auditing storage techniques have been developed to overcome the data deduplication (DD) problem, but they are vulnerable and can't resist brute force attacks (BFA). There is some privacy leakage problem that occurred in the present method. In this article, an original strategy called domain-user integra tag (DUIT) has been presented which comprises inter and intra deduplication with file tag and symmetric encryption key

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Tian, Guohua, Hua Ma, Ying Xie, and Zhenhua Liu. "Randomized deduplication with ownership management and data sharing in cloud storage." Journal of Information Security and Applications 51 (April 2020): 102432. http://dx.doi.org/10.1016/j.jisa.2019.102432.

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Kwon, Hyunsoo, Changhee Hahn, Kyungtae Kang, and Junbeom Hur. "Secure deduplication with reliable and revocable key management in fog computing." Peer-to-Peer Networking and Applications 12, no. 4 (2018): 850–64. http://dx.doi.org/10.1007/s12083-018-0682-9.

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Kaur, Ravneet, Inderveer Chana, and Jhilik Bhattacharya. "Data deduplication techniques for efficient cloud storage management: a systematic review." Journal of Supercomputing 74, no. 5 (2017): 2035–85. http://dx.doi.org/10.1007/s11227-017-2210-8.

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Zhou, Lixi, Jiaqing Chen, Amitabh Das, et al. "Serving deep learning models with deduplication from relational databases." Proceedings of the VLDB Endowment 15, no. 10 (2022): 2230–43. http://dx.doi.org/10.14778/3547305.3547325.

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Serving deep learning models from relational databases brings significant benefits. First, features extracted from databases do not need to be transferred to any decoupled deep learning systems for inferences, and thus the system management overhead can be significantly reduced. Second, in a relational database, data management along the storage hierarchy is fully integrated with query processing, and thus it can continue model serving even if the working set size exceeds the available memory. Applying model deduplication can greatly reduce the storage space, memory footprint, cache misses, an

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R. Vignesh, R. Vignesh, and J. Preethi R. Vignesh. "Secure Data Deduplication System with Efficient and Reliable Multi-Key Management in Cloud Storage." 網際網路技術學刊 23, no. 4 (2022): 811–25. http://dx.doi.org/10.53106/160792642022072304016.

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<p>The revolutionary growth in the processing and storage mechanisms over the Internet has given the enhancement to inexpensive and strong computing properties. Cloud computing is a rising technology, which offers the data storage facility also application accessing facility in online environment. This system stands countless opportunities also challenges. In that, security of data and the increasing similar data in cloud (duplication) are very important issues to be addressed. So, Deduplication method is developed to reduce the similar data that is present in the storage system. In this

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Meenambigai., D., and V. Mathavan. "User Data Integrating with Anti Collusion Scheme and SVC in Cloud Groups." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1039–44. https://doi.org/10.31142/ijtsrd9607.

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In cloud computing, user can share data among group members with the characters of less maintenance and little management cost. Sharing data must have security guarantees, if they are out sourced. Sharing data while providing privacy preserving is still a challenging problem, when change of the membership. It might cause to the collusion attack for an unsecured cloud. For existing technique, security of key distribution is based on the secure communication channel, however, to have such channel is a strong assumption and is difficult for practice. We propose a secure data sharing scheme for dy

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S, Janakiraman. "Cloud Clean: Cloud Storage Space Sustainability using Data Life Cycle Guard Scheme." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 165–72. https://doi.org/10.22214/ijraset.2025.71988.

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Junk files, including outdated backups, redundant document versions, and orphaned objects, accumulate in cloud storage, leading to inefficiencies in data retrieval, increased latency, and higher storage costs. As cloud applications grow in scale, managing and optimizing storage resources becomes crucial for maintaining performance and reducing operational overhead. The problem of unnecessary files taking up valuable space is especially critical in cloud environments where efficient resource management is essential for smooth operations. This project proposes a solution to optimize cloud data m

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Singh, A. "Secure Auditing and Deduplicating Data in Cloud." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6530–34. http://dx.doi.org/10.22214/ijraset.2023.53343.

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Abstract: With the widespread adoption of cloud computing, ensuring the security and integrity of data stored in the cloud has become a critical concern. This paper presents a novel approach for achieving secure auditing and deduplication of data in cloud environments. The proposed method addresses the challenges of data integrity verification and duplicate elimination while preserving the privacy of the stored data. By leveraging cryptographic techniques, including homomorphic encryption and Merkle hash trees, the system enables efficient and privacy- preserving auditing of data integrity. Ad

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Sohail, Asif, and Syed Waqar Jaffry. "Ranking the Blocking Keys for Data Deduplication in Information Systems." International Journal of Business Information Systems 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijbis.2021.10044941.

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