scholarly journals Performance Evaluation of RSA-based Secure Cloud Storage Protocol using OpenStack

2021 ◽  
Vol 11 (4) ◽  
pp. 7321-7325
Author(s):  
M. F. Hyder ◽  
S. Tooba ◽  
. Waseemullah
Keyword(s):  
Cloud Storage ◽  
Performance Metrics ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Cloud Environment ◽  
Computation Cost ◽  
Secure Storage ◽  
Secure Cloud Storage ◽  
Series Of Experiments ◽  
And Performance

In this paper, the implementation of the General Secure Cloud Storage Protocol is carried out and instantiated by a multiplicatively Homomorphic Encryption Scheme (HES). The protocol provides a system for secure storage of data over the cloud, thereby allowing the client to carry out the operational tasks on it efficiently. The work focuses on the execution of five major modules of the protocol. We also evaluate the performance of the protocol with respect to the computation cost of these modules on the basis of different security parameters and datasets by conducting a series of experiments. The cloud was built using OpenStack and the data were outsourced from the client’s system to the cloud to study the security features and performance metrics when adopting the cloud environment.

A general framework to design secure cloud storage protocol using homomorphic encryption scheme

2017 ◽  
Vol 129 ◽  
pp. 37-50 ◽  
Author(s):  
Jian Zhang ◽  
Yang Yang ◽  
Yanjiao Chen ◽  
Jing Chen ◽  
Qian Zhang
Keyword(s):  
Cloud Storage ◽  
General Framework ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Secure Cloud Storage

Implementation and Performance Evaluation of RNS Variants of the BFV Homomorphic Encryption Scheme

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Ahmad Qaisar Ahmad Al Badawi ◽  
Yuriy Polyakov ◽  
Khin Mi Mi Aung ◽  
Bharadwaj Veeravalli ◽  
Kurt Rohloff
Keyword(s):  
Performance Evaluation ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
And Performance

scholarly journals Homomorphic Cryptosystems for Data Security in Cloud Storage

2019 ◽  
Vol 9 (2) ◽  
pp. 3164-3166
Keyword(s):  
Cloud Storage ◽  
Homomorphic Encryption ◽  
Data Authentication ◽  
Outsourced Data ◽  
Integrity Checking ◽  
Data Owner ◽  
Encryption Schemes ◽  
Verification Process ◽  
And Performance ◽  
Local Storage

Cloud Computing enables users to use remote resources thus reduces the burden on local storage. However, the use of such services gives rise to new set of problems. The users have no control over the data which they have stored on those storages so to achieve data authentication with confidentiality is utmost important. As every user may not have that expertise so they can request for data verification task to Trusted Verifier (TV) which will be an authorized party to check the intactness of outsourced data. Since the data owner stores the data on the cloud in an encrypted format, it becomes difficult to check the integrity of the data without decrypting. But by using homomorphic encryption schemes the integrity checking can be made possible without original copy. In this paper, we have given implementation and performance details of two homomorphic encryption schemes, Rivest Shamir Adleman (RSA) and Paillier. The RSA is multiplicative homomorphic scheme where the Paillier is additive homomorphic scheme. Both the algorithms are partially homomorphic thus limited in their functions. Due to homomorphic property of these algorithms, original contents will not get revealed in the verification process. This framework will achieve authentication of data by maintaining confidentiality.

scholarly journals Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1172 ◽  
Author(s):  
Kanwal Janjua ◽  
Munam Ali Shah ◽  
Ahmad Almogren ◽  
Hasan Ali Khattak ◽  
Carsten Maple ◽  
...  
Keyword(s):  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Security And Privacy ◽  
Raspberry Pi ◽  
Test Bed ◽  
Secure Storage ◽  
Cloud Models ◽  
And Performance ◽  
Multi Stakeholder ◽  
And Control

Collecting and preserving the smart environment logs connected to cloud storage is challenging due to the black-box nature and the multi-tenant cloud models which can pervade log secrecy and privacy. The existing work for log secrecy and confidentiality depends on cloud-assisted models, but these models are prone to multi-stakeholder collusion problems. This study proposes ’PLAF,’ a holistic and automated architecture for proactive forensics in the Internet of Things (IoT) that considers the security and privacy-aware distributed edge node log preservation by tackling the multi-stakeholder issue in a fog enabled cloud. We have developed a test-bed to implement the specification, as mentioned earlier, by incorporating many state-of-the-art technologies in one place. We used Holochain to preserve log integrity, provenance, log verifiability, trust admissibility, and ownership non-repudiation. We introduced the privacy preservation automation of log probing via non-malicious command and control botnets in the container environment. For continuous and robust integration of IoT microservices, we used docker containerization technology. For secure storage and session establishment for logs validation, Paillier Homomorphic Encryption, and SSL with Curve25519 is used respectively. We performed the security and performance analysis of the proposed PLAF architecture and showed that, in stress conditions, the automatic log harvesting running in containers gives a 95% confidence interval. Moreover, we show that log preservation via Holochain can be performed on ARM-Based architectures such as Raspberry Pi in a very less amount of time when compared with RSA and blockchain.

Privacy-Preserving Public Auditing and Data Dynamics for Secure Cloud Storage Based on Exact Regenerated Code

2019 ◽  
Vol 9 (4) ◽  
pp. 1-20
Author(s):  
Syam Kumar Pasupuleti
Keyword(s):  
Cloud Storage ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Public Auditing ◽  
Data Dynamics ◽  
Secure Cloud Storage ◽  
And Performance ◽  
Local Storage ◽  
Recovery Problem ◽  
Provably Secure

Cloud storage allows users to store their data in the cloud to avoid local storage and management costs. Since the cloud is untrusted, the integrity of stored data in the cloud has become an issue. To address this problem, several public auditing schemes have been designed to verify integrity of the data in the cloud. However, these schemes have two drawbacks: public auditing may reveal sensitive data to verifier and does not address the data recovery problem efficiently. This article proposes a new privacy-preserving public auditing scheme with data dynamics to secure the data in the cloud based on an exact regenerated code. This scheme encodes the data for availability, then masks the encoded blocks with randomness for privacy of data and enables a public auditor to verify the integrity of the data. Further, this scheme also supports dynamic data updates. In addition, security and performance analysis proves that proposed scheme is provably secure and efficient.

Privacy-Preserving Public Auditing and Data Dynamics for Secure Cloud Storage Based on Exact Regenerated Code

2021 ◽  
pp. 1003-1022
Author(s):  
Syam Kumar Pasupuleti
Keyword(s):  
Cloud Storage ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Dynamic Data ◽  
Public Auditing ◽  
Data Dynamics ◽  
Secure Cloud Storage ◽  
And Performance ◽  
Local Storage ◽  
Recovery Problem

Cloud storage allows users to store their data in the cloud to avoid local storage and management costs. Since the cloud is untrusted, the integrity of stored data in the cloud has become an issue. To address this problem, several public auditing schemes have been designed to verify integrity of the data in the cloud. However, these schemes have two drawbacks: public auditing may reveal sensitive data to verifier and does not address the data recovery problem efficiently. This article proposes a new privacy-preserving public auditing scheme with data dynamics to secure the data in the cloud based on an exact regenerated code. This scheme encodes the data for availability, then masks the encoded blocks with randomness for privacy of data and enables a public auditor to verify the integrity of the data. Further, this scheme also supports dynamic data updates. In addition, security and performance analysis proves that proposed scheme is provably secure and efficient.

scholarly journals Privacy-Preserving Oriented Floating-Point Number Fully Homomorphic Encryption Scheme

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Shuangjie Bai ◽  
Geng Yang ◽  
Jingqi Shi ◽  
Guoxiu Liu ◽  
Zhaoe Min
Keyword(s):  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Expansion Ratio ◽  
Public Key ◽  
Floating Point ◽  
Encryption Scheme ◽  
Cloud Environment ◽  
Fully Homomorphic Encryption ◽  
Point Number ◽  
Floating Point Number

The issue of the privacy-preserving of information has become more prominent, especially regarding the privacy-preserving problem in a cloud environment. Homomorphic encryption can be operated directly on the ciphertext; this encryption provides a new method for privacy-preserving. However, we face a challenge in understanding how to construct a practical fully homomorphic encryption on non-integer data types. This paper proposes a revised floating-point fully homomorphic encryption scheme (FFHE) that achieves the goal of floating-point numbers operation without privacy leakage to unauthorized parties. We encrypt a matrix of plaintext bits as a single ciphertext to reduce the ciphertext expansion ratio and reduce the public key size by encrypting with a quadratic form in three types of public key elements and pseudo-random number generators. Additionally, we make the FFHE scheme more applicable by generalizing the homomorphism of addition and multiplication of floating-point numbers to analytic functions using the Taylor formula. We prove that the FFHE scheme for ciphertext operation may limit an additional loss of accuracy. Specifically, the precision of the ciphertext operation’s result is similar to unencrypted floating-point number computation. Compared to other schemes, our FFHE scheme is more practical for privacy-preserving in the cloud environment with its low ciphertext expansion ratio and public key size, supporting multiple operation types and high precision.

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