Research perspectives on fully homomorphic encryption models for cloud sector

2021 ◽  
pp. 1-26
Author(s):  
Sonam Mittal ◽  
K.R. Ramkumar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Homomorphic Encryption ◽  
Data Encryption ◽  
Cloud Services ◽  
Future Research ◽  
Public Cloud ◽  
Fully Homomorphic Encryption ◽  
Research Perspectives ◽  
Learning With Errors

As there is a continuous delivery of big data, the researchers are showing interest in the applications of cloud computing concerning privacy, and security. On the other hand, many researchers and experts of cybersecurity have commenced on a quest for improving the data encryption to the models of big data and applications of cloud computing. Since many users of the cloud become public cloud services, confidentiality turns out to be a more compound problem. To solve the confidentiality problem, cloud clients maintain the data on the public cloud. Under this circumstance, Homomorphic Encryption (HE) appears as a probable solution, in which the information of the client is encrypted on the cloud in such a process that it permits few manipulation operations without decryption. The main intent of this paper is to present the systematic review of research papers published in the field of Fully Homomorphic Encryption (FHE) over the past 10 years. The encryption scheme is considered full when it consists of plaintext, a ciphertext, a keyspace, an encryption algorithm, and a decryption algorithm. Hence, the review mostly concentrates on reviewing more powerful and recent FHE. The contributions using different algorithms in FHE like Lattice-based, integer-based, Learning With Errors (LWE), Ring Learning With Errors (RLWE), and Nth degree Truncated polynomial Ring Units (NTRU) are also discussed. Finally, it highlights the challenges and gaps to be addressed in modeling and learning about competent, effectual, and vigorous FHE for the cloud sector and pays attention to directions for better future research.

scholarly journals Improved Cryptanalysis of a Fully Homomorphic Symmetric Encryption Scheme

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Quanbo Qu ◽  
Baocang Wang ◽  
Yuan Ping ◽  
Zhili Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Symmetric Encryption ◽  
Secret Key ◽  
Fully Homomorphic Encryption ◽  
Key Recovery ◽  
Key Recovery Attack ◽  
Outsourced Databases

Homomorphic encryption is widely used in the scenarios of big data and cloud computing for supporting calculations on ciphertexts without leaking plaintexts. Recently, Li et al. designed a symmetric homomorphic encryption scheme for outsourced databases. Wang et al. proposed a successful key-recovery attack on the homomorphic encryption scheme but required the adversary to know some plaintext/ciphertext pairs. In this paper, we propose a new ciphertext-only attack on the symmetric fully homomorphic encryption scheme. Our attack improves the previous Wang et al.’s attack by eliminating the assumption of known plaintext/ciphertext pairs. We show that the secret key of the user can be recovered by running lattice reduction algorithms twice. Experiments show that the attack successfully and efficiently recovers the secret key of the randomly generated instances with an overwhelming probability.

scholarly journals A Hybrid Improved Zhou and Wornell’s Inspired Fully Homomorphic Encryption Scheme for Securing Big Data Computation in Cloud Environment

2021 ◽  
Author(s):  
Nithiavathy R ◽  
Vanitha K ◽  
Manimaran A ◽  
Ilampiray P ◽  
Alaguvathana P
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Homomorphic Encryption ◽  
Cloud Service ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
Bounded Degree ◽  
Cloud Computing Environment ◽  
High Concern ◽  
Low Dimensional

Abstract The process of performing smart computations in the big data and cloud computing environment is considered to be highly essential in spite of its complexity and cost. The method of Fully Homomorphic encryption is considered to be the effective approach that provides the option of working with the encrypted form of sensitive data in order to preserve high confidentiality that concentrates on deriving benefits from cloud computing capabilities. In this paper, a Hybrid Improved Zhou and Wornell’s inspired Fully Homomorphic Encryption (HIZWFHE) Scheme is proposed for securing big data computation, when they are outsourced to cloud service. This HIZWFHE scheme is potent in encrypting integer vectors that permit the computation of big data represented in the contextual polynomial form in the encrypted form with a bounded degree of limits. This HIZWFHE scheme is determined to be highly applicable and suitable and applicable in cloud big data computation in which the learning process of low dimensional representations is of high concern.

Lattice-Based Identity-Based Homomorphic Conditional Proxy Re-Encryption for Secure Big Data Computing in Cloud Environment

2017 ◽  
Vol 28 (06) ◽  
pp. 645-660 ◽  
Author(s):  
Chunguang Ma ◽  
Juyan Li ◽  
Weiping Ouyang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Sharing ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Cloud Environment ◽  
Public Cloud ◽  
Encrypted Data ◽  
Identity Based ◽  
Big Data Computing

With the arrival of the era of big data, more and more users begin to adopt public cloud storage to store data and compute data. Sharing large amounts of sensitive data in the public cloud will arouse privacy concerns. Data encryption is a widely accepted method to prevent information leakage. How to achieve the cloud sharing and cloud computing of big data is a challenging problem. Conditional proxy re-encryption can solve cloud sharing, and homomorphic encryption can achieve cloud computing. In this paper, we combine conditional proxy re-encryption with homomorphic encryption to construct a lattice-based identity-based homomorphic conditional proxy re-encryption for secure big data computing in cloud environment. The scheme can not only realize the encrypted data sharing in the cloud, but also can realize the encrypted data computing in the cloud. That is, the homomorphic conditional proxy re-encryption scheme can homomorphically evaluate ciphertexts no matter ciphertexts are “fresh” or re-encrypted (re-encrypted ciphertexts can come from different identities). The constructed scheme modifies the homomorphic proxy re-encryption scheme of Ma et al. We also use the approximate eigenvector method to manage the noise level and decrease the decryption complexity without introducing additional assumptions. At last, we prove that the scheme is indistinguishable against chosen-plaintext attacks, key privacy secure and master secret secure.

A Neural Network Application of Fully Homomorphic Encryption for Cloud Computing

2020 ◽  
Author(s):  
Megha Kolhekar ◽  
Ashish Pandey ◽  
Ayushi Raina ◽  
Rijin Thomas ◽  
Vaibhav Tiwari ◽  
...  
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fully Homomorphic Encryption ◽  
Network Application

scholarly journals Cloud computing security: a survey

2018 ◽  
Vol 7 (2.21) ◽  
pp. 355
Author(s):  
P Sheela Gowr ◽  
N Kumar
Keyword(s):  
Cloud Computing ◽  
Information Flow ◽  
Data Encryption ◽  
Information Services ◽  
Ease Of Use ◽  
Cloud Services ◽  
Authentication Scheme ◽  
Data Confidentiality ◽  
Cloud Computing Security ◽  
Security Issues

Cloud computing was a hasting expertise which has innovated to a collection of new explores. A sub-ordinate device for Information services, it has an ability towards encourage development by feeding convenient environments for a choice of forms of development is different sequence. Clouds usually consider being eco-friendly, however keep it has open to the diversity of some security issues to can change together the feeder as well as users of these cloud services. In this issue are principally associated to the protection of the information flow throughout also being store in the cloud, with simple problems along with data ease of use, data right to use and data confidentiality. Data encryption and service authentication scheme has been initiated by the industries to deal with them. In this paper analyse and examine different issues on security beside with the different procedure worn by the industries to solve these effects. 

scholarly journals Big Data using Cloud Computing - Opportunities for Small and Medium-sized Enterprises

2016 ◽  
Vol 4 (1) ◽  
pp. 129 ◽  
Author(s):  
Narasimha Rao Vajjhala ◽  
Ervin Ramollari
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Fault Tolerance ◽  
Competitive Advantage ◽  
Computer Architecture ◽  
Financial Resources ◽  
Future Research ◽  
Current Trends ◽  
Low Performance ◽  
Research Frontiers

Big Data has been listed as one of the current and future research frontiers by Gartner. Large-sized companies are already investing on and leveraging big data. Small-sized and medium-sized enterprises (SMEs) can also leverage big data to gain a strategic competitive advantage but are often limited by the lack of adequate financial resources to invest on the technology and manpower. Several big data challenges still exist especially in computer architecture that is CPU-heavy but I/O poor. Cloud computing eliminates the need to maintain expensive computing hardware and software. Cloud computing resources and techniques can be leveraged to address the traditional problems associated with fault tolerance and low performance causing bottlenecks to using big data. SMEs can take advantage of cloud computing techniques to avail the advantages of big data without significant investments in technology and manpower. This paper explores the current trends in the area of big data using cloud resources and how SMEs can take advantage of these technological trends. The results of this study will benefit SMEs in identifying and exploring possible opportunities and also understanding the challenges in leveraging big data.

A Security-By-Distribution Approach to Manage Big Data in a Federation of Untrustworthy Clouds

2019 ◽  
pp. 346-375
Author(s):  
Jens Kohler ◽  
Christian Richard Lorenz ◽  
Markus Gumbel ◽  
Thomas Specht ◽  
Kiril Simov
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cost Savings ◽  
Cloud Provider ◽  
Small Data ◽  
Storage Space ◽  
Public Cloud ◽  
Infrastructure Investments ◽  
Security Compliance ◽  
Document Stores

In recent years, Cloud Computing has drastically changed IT-Architectures in enterprises throughout various branches and countries. Dynamically scalable capabilities like CPUs, storage space, virtual networks, etc. promise cost savings, as huge initial infrastructure investments are not required anymore. This development shows that Cloud Computing is also a promising technology driver for Big Data, as the storage of unstructured data when no concrete and defined data schemes (variety) can be managed with upcoming NoSQL architectures. However, in order to fully exploit these advantages, the integration of a trustworthy 3rd party public cloud provider is necessary. Thus, challenging questions concerning security, compliance, anonymization, and privacy emerge and are still unsolved. To address these challenges, this work presents, implements and evaluates a security-by-distribution approach for NoSQL document stores that distributes data across various cloud providers such that every provider only gets a small data chunk which is worthless without the others.

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