scholarly journals Enhanced Integrity Checking for Preserve Data Owner and User Level Privacy Using Dual Cryptography Approach

2019 ◽  
pp. 138-146
Author(s):  
Poovizhi. M ◽  
Raja. G
Keyword(s):  
Data Storage ◽  
Data Privacy ◽  
Capital Expenditure ◽  
Data Access ◽  
Third Party ◽  
Configurable Computing ◽  
Local Data ◽  
Cloud Data ◽  
On Demand ◽  
Integrity Checking

Using Cloud Storage, users can tenuously store their data and enjoy the on-demand great quality applications and facilities from a shared pool of configurable computing resources, without the problem of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in Cloud Computing a formidable task, especially for users with constrained dividing resources. From users’ perspective, including both individuals and IT systems, storing data remotely into the cloud in a flexible on-demand manner brings tempting benefits: relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances, etc. To securely introduce an effective Sanitizer and third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to capably audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should take in no new vulnerabilities towards user data privacy. In this project, utilize and uniquely combine the public auditing protocols with double encryption approach to achieve the privacy-preserving public cloud data auditing system, which meets all integrity checking without any leakage of data. To support efficient handling of multiple auditing tasks, we further explore the technique of online signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. We can implement double encryption algorithm to encrypt the data twice and stored cloud server in Electronic Health Record applications.

scholarly journals Data Privacy Preservation using AES-GCM Encryption in Heroku Cloud

2019 ◽  
Vol 8 (3) ◽  
pp. 7544-7548
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Service Industry ◽  
Computational Cost ◽  
Cloud Service ◽  
Third Party ◽  
Sensitive Information ◽  
Cloud Data

The increasing popularity of cloud data storage and its ever-rising versatility, shows that cloud computing is one of the most widely excepted phenomena. It not only helps with powerful computing facilities but also reduce a huge amount of computational cost. And with such high demand for storage has raised the growth of the cloud service industry that provides an affordable, easy-to-use and remotely-accessible services. But like every other emerging technology it carries some inherent security risks associated and cloud storage is no exception. The prime reason behind it is that users have to blindly trust the third parties while storing the useful information, which may not work in the best of interest. Hence, to ensure the privacy of sensitive information is primarily important for any public, third-party cloud. In this paper, we mainly focus on proposing a secure cloud framework with encrypting sensitive data’s using AES-GCM cryptographic techniques in HEROKU cloud platform. Here we tried to implement Heroku as a cloud computing platform, used the AES-GCM algorithm and evaluate the performance of the said algorithm. Moreover, analyses the performance of AES/GCM execution time with respect to given inputs of data

scholarly journals A New Hybrid Automated Security Framework to Cloud Storage System

Cryptography ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 37
Author(s):  
Noha E. El-Attar ◽  
Doaa S. El-Morshedy ◽  
Wael A. Awad
Keyword(s):  
Data Storage ◽  
Cloud Storage ◽  
Data Privacy ◽  
High Performance ◽  
Storage System ◽  
Third Party ◽  
File Size ◽  
Cloud Data ◽  
Security Issues ◽  
User Data

The need for cloud storage grows day after day due to its reliable and scalable nature. The storage and maintenance of user data at a remote location are severe issues due to the difficulty of ensuring data privacy and confidentiality. Some security issues within current cloud systems are managed by a cloud third party (CTP), who may turn into an untrustworthy insider part. This paper presents an automated Encryption/Decryption System for Cloud Data Storage (AEDS) based on hybrid cryptography algorithms to improve data security and ensure confidentiality without interference from CTP. Three encryption approaches are implemented to achieve high performance and efficiency: Automated Sequential Cryptography (ASC), Automated Random Cryptography (ARC), and Improved Automated Random Cryptography (IARC) for data blocks. In the IARC approach, we have presented a novel encryption strategy by converting the static S-box in the AES algorithm to a dynamic S-box. Furthermore, the algorithms RSA and Twofish are used to encrypt the generated keys to enhance privacy issues. We have evaluated our approaches with other existing symmetrical key algorithms such as DES, 3DES, and RC2. Although the two proposed ARC and ASC approaches are more complicated, they take less time than DES, DES3, and RC2 in processing the data and obtaining better performance in data throughput and confidentiality. ARC outperformed all of the other algorithms in the comparison. The ARC’s encrypting process has saved time compared with other algorithms, where its encryption time has been recorded as 22.58 s for a 500 MB file size, while the DES, 3DES, and RC2 have completed the encryption process in 44.43, 135.65, and 66.91 s, respectively, for the same file size. Nevertheless, when the file sizes increased to 2.2 GB, the ASC proved its efficiency in completing the encryption process in less time.

Control Cloud Data Access Privilge Anonymity with Attributed Based Encryption

2017 ◽  
Vol 7 (8) ◽  
pp. 279
Author(s):  
P. Sudheer ◽  
T. Lakshmi Surekha
Keyword(s):  
Data Privacy ◽  
Low Cost ◽  
Data Access ◽  
Privacy Concerns ◽  
Cloud Data ◽  
Computing Paradigm ◽  
Attribute Based Encryption ◽  
Data Content ◽  
Identity Privacy ◽  
Cloud Servers

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The  Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.

A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Service ◽  
Third Party ◽  
Group Collaboration ◽  
Cloud Data ◽  
Cloud Data Storage ◽  
Important Trend ◽  
Data Update ◽  
Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

scholarly journals An enhanced security tree to secure cloud data

2017 ◽  
Vol 7 (1.1) ◽  
pp. 64 ◽  
Author(s):  
S. Renu ◽  
S.H. Krishna Veni
Keyword(s):  
Data Storage ◽  
Binary Tree ◽  
Data Encryption ◽  
Third Party ◽  
Data Outsourcing ◽  
Cloud Data ◽  
Security Issues ◽  
Security Services ◽  
Computing Services ◽  
User Data

The Cloud computing services and security issues are growing exponentially with time. All the CSPs provide utmost security but the issues still exist. Number of technologies and methods are emerged and futile day by day. In order to overcome this situation, we have also proposed a data storage security system using a binary tree approach. Entire services of the binary tree are provided by a Trusted Third Party (TTP) .TTP is a government or reputed organization which facilitates to protect user data from unauthorized access and disclosure. The security services are designed and implemented by the TTP and are executed at the user side. Data classification, Data Encryption and Data Storage are the three vital stages of the security services. An automated file classifier classify unorganized files into four different categories such as Sensitive, Private, Protected and Public. Applied cryptographic techniques are used for data encryption. File splitting and multiple cloud storage techniques are used for data outsourcing which reduces security risks considerably. This technique offers  file protection even when the CSPs compromise. 

Privacy Preserving and Efficient Outsourcing Algorithm to Public Cloud

2021 ◽  
pp. 857-878
Author(s):  
Malay Kumar ◽  
Manu Vardhan
Keyword(s):  
Data Privacy ◽  
Classification Systems ◽  
Third Party ◽  
Cloud Data ◽  
Computing Services ◽  
Cloud Server ◽  
Cloud Computing Services ◽  
Cloud Data Centers ◽  
Outsourcing Algorithm ◽  
Direct Implementation

The growth of the cloud computing services and its proliferation in business and academia has triggered enormous opportunities for computation in third-party data management settings. This computing model allows the client to outsource their large computations to cloud data centers, where the cloud server conducts the computation on their behalf. But data privacy and computational integrity are the biggest concern for the client. In this article, the authors attempt to present an algorithm for secure outsourcing of a covariance matrix, which is the basic building block for many automatic classification systems. The algorithm first performs some efficient transformation to protect the privacy and verify the computed result produced by the cloud server. Further, an analytical and experimental analysis shows that the algorithm is simultaneously meeting the design goals of privacy, verifiability and efficiency. Also, found that the proposed algorithm is about 7.8276 times more efficient than the direct implementation.

scholarly journals A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Yeong-Cherng Hsu ◽  
Chih-Hsin Hsueh ◽  
Ja-Ling Wu
Keyword(s):  
Data Privacy ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Data Access ◽  
Privacy Preserving ◽  
Secret Key ◽  
K Nearest Neighbor ◽  
Sensitive Data ◽  
Cloud Data ◽  
Cloud Server

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

Privacy Preserving and Efficient Outsourcing Algorithm to Public Cloud

2018 ◽  
Vol 12 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Malay Kumar ◽  
Manu Vardhan
Keyword(s):  
Data Privacy ◽  
Classification Systems ◽  
Third Party ◽  
Cloud Data ◽  
Computing Model ◽  
Computing Services ◽  
Cloud Server ◽  
Cloud Computing Services ◽  
Cloud Data Centers ◽  
Outsourcing Algorithm

The growth of the cloud computing services and its proliferation in business and academia has triggered enormous opportunities for computation in third-party data management settings. This computing model allows the client to outsource their large computations to cloud data centers, where the cloud server conducts the computation on their behalf. But data privacy and computational integrity are the biggest concern for the client. In this article, the authors attempt to present an algorithm for secure outsourcing of a covariance matrix, which is the basic building block for many automatic classification systems. The algorithm first performs some efficient transformation to protect the privacy and verify the computed result produced by the cloud server. Further, an analytical and experimental analysis shows that the algorithm is simultaneously meeting the design goals of privacy, verifiability and efficiency. Also, found that the proposed algorithm is about 7.8276 times more efficient than the direct implementation.

scholarly journals Controlled Sharing Mechanism of Data Based on the Consortium Blockchain

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jin Li ◽  
Songqi Wu ◽  
Yundan Yang ◽  
Fenghui Duan ◽  
Hui Lu ◽  
...  
Keyword(s):  
Data Storage ◽  
Data Flow ◽  
Electric Energy ◽  
Electrical Energy ◽  
Data Access ◽  
Original Data ◽  
Third Party ◽  
Data Accessibility ◽  
Energy Data ◽  
Data Access Control

In the process of sharing data, the costless replication of electric energy data leads to the problem of uncontrolled data and the difficulty of third-party access verification. This paper proposes a controlled sharing mechanism of data based on the consortium blockchain. The data flow range is controlled by the data isolation mechanism between channels provided by the consortium blockchain by constructing a data storage consortium chain to achieve trusted data storage, combining attribute-based encryption to complete data access control and meet the demands for granular data accessibility control and secure sharing; the data flow transfer ledger is built to record the original data life cycle management and effectively record the data transfer process of each data controller. Taking the application scenario of electric energy data sharing as an example, the scheme is designed and simulated on the Linux system and Hyperledger Fabric. Experimental results have verified that the mechanism can effectively control the scope of access to electrical energy data and realize the control of the data by the data owner.

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