Block Level Data-Deduplication and Security Using Convergent Encryption to Offer Proof of Verification

2020 ◽  
Author(s):  
B. Tirapathi Reddy ◽  
P. Sai Kiran ◽  
T. Priyanandan ◽  
CH. Vikas Chowdary ◽  
B. Jaya Aditya
Keyword(s):  
Data Deduplication ◽  
Level Data ◽  
Block Level

scholarly journals Deduplication in Cloud Storage

2019 ◽  
Vol 9 (2S) ◽  
pp. 364-368
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Cloud Service ◽  
Information Storage ◽  
Cloud Environment ◽  
Data Deduplication ◽  
Cloud Service Provider ◽  
Level Data ◽  
Block Level ◽  
Individual Client

Cloud Computing is well known today on account of enormous measure of data storage and quick access of information over the system. It gives an individual client boundless extra space, accessibility and openness of information whenever at anyplace. Cloud service provider can boost information storage by incorporating data deduplication into cloud storage, despite the fact that information deduplication removes excess information and reproduced information happens in cloud environment. This paper presents a literature survey alongside different deduplication procedures that have been based on cloud information storage. To all the more likely guarantee secure deduplication in cloud, this paper examines file level data deduplication and block level data deduplication.

scholarly journals Geography of racial coresidence

2021 ◽  
Author(s):  
Anna Dmowska ◽  
Tomasz Stepinski
Keyword(s):  
Metropolitan Areas ◽  
Data Driven ◽  
Racial Groups ◽  
Census Block ◽  
Asian Populations ◽  
Racial Preferences ◽  
Level Data ◽  
Geographical Regions ◽  
Block Level ◽  
Us Cities

Whereas most work on residential race relations in US cities is based on the concept of segregation, our approach studies this issue from a who-lives-with-whom perspective. To this end, we study coresidence profiles – percentages of a given racial subpopulation living in different population zones. Population zones are data-driven divisions of a city based on characteristic racial compositions. We used 1990 and 2010 decennial census block-level data for 61 largest US metropolitan areas to calculate coresidence profiles for four major racial subpopulations in each city at both years. Profiles for each race/year combination were clustered into three archetypes. Cities, where given race profiles belong to the same archetype, have similar coresidence patterns with respect to this race. We present the geographic distributions of co-habitation profiles and show how they changed during the 1990-2010 period. Our results revealed that coresidence profiles depend not only on racial preferences but also on the availability of racial groups; cities in the different geographical regions have different coresidence profiles because they have different shares of White, Black, Hispanic, and Asian subpopulations. Temporal changes in coresidence profiles are linked to the increased share of Hispanic and Asian populations.

scholarly journals Security Analysis and Preserving Block-Level Data DE-duplication in Cloud Storage Services

2020 ◽  
Vol 2 (2) ◽  
pp. 120-126 ◽  
Author(s):  
Adithya M. ◽  
Dr. Shanthini B.
Keyword(s):  
Distributed Storage ◽  
Security Analysis ◽  
Space Design ◽  
Secure Information ◽  
Information Accessibility ◽  
Level Data ◽  
Block Level ◽  
Potential Applications ◽  
Cloud Storage Services ◽  
Current Plan

Secure information deduplication can altogether decrease the correspondence and capacity overheads in distributed storage benefits and has potential applications in our large information-driven society. Existing information deduplication plans are commonly intended to either oppose savage power assaults or guarantee the effectiveness and information accessibility, yet not the two conditions. We are additionally not mindful of any current plan that accomplishes responsibility, in the feeling of lessening copy data divulgence (e.g., to decide if plain-writings of two scrambled messages are indistinguishable). Right now, examine the three-level cross-space design and propose an effective and protection safeguarding huge information deduplication in distributed storage (from now on alluded to as EPCDD). EPCDD accomplishes both protection safeguarding and information accessibility and opposes beast power assaults. Plus, we consider the responsibility to offer preferable protection affirmations over existing plans. We at that point show that EPCDD beats existing contending plans, as far as calculation, correspondence, and capacity overheads. Additionally, the time unpredictability of copy search in EPCDD is logarithmic.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Yinghui Zhang ◽  
Haonan Su ◽  
Menglei Yang ◽  
Dong Zheng ◽  
Fang Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Security Analysis ◽  
Operation Time ◽  
Communication Cost ◽  
Wireless Sensing ◽  
Data Deduplication ◽  
Sensing Data ◽  
Storage Overhead ◽  
Block Level ◽  
Cloud Users

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.

scholarly journals A secure data deduplication system for integrated cloud-edge networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shynu P. G. ◽  
Nadesh R. K. ◽  
Varun G. Menon ◽  
Venu P. ◽  
Mahdi Abbasi ◽  
...  
Keyword(s):  
Elliptic Curve ◽  
Elliptic Curve Cryptography ◽  
Cloud Storage ◽  
Data Deduplication ◽  
Data Redundancy ◽  
Systems Implementation ◽  
Block Level ◽  
Cloud Environments ◽  
Security Levels ◽  
Edge Networks

AbstractData redundancy is a significant issue that wastes plenty of storage space in the cloud-fog storage integrated environments. Most of the current techniques, which mainly center around the static scenes, for example, the backup and archive systems, are not appropriate because of the dynamic nature of data in the cloud or integrated cloud environments. This problem can be effectively reduced and successfully managed by data deduplication techniques, eliminating duplicate data in cloud storage systems. Implementation of data deduplication (DD) over encrypted data is always a significant challenge in an integrated cloud-fog storage and computing environment to optimize the storage efficiently in a highly secured manner. This paper develops a new method using Convergent and Modified Elliptic Curve Cryptography (MECC) algorithms over the cloud and fog environment to construct secure deduplication systems. The proposed method focuses on the two most important goals of such systems. On one side, the redundancy of data needs to be reduced to its minimum, and on the other hand, a robust encryption approach must be developed to ensure the security of the data. The proposed technique is well suited for operations such as uploading new files by a user to the fog or cloud storage. The file is first encrypted using the Convergent Encryption (CE) technique and then re-encrypted using the Modified Elliptic Curve Cryptography (MECC) algorithm. The proposed method can recognize data redundancy at the block level, reducing the redundancy of data more effectively. Testing results show that the proposed approach can outperform a few state-of-the-art methods of computational efficiency and security levels.

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