A verifiable fully homomorphic encryption scheme to secure big data in cloud computing

2017 ◽  
Author(s):  
Ahmed El-Yahyaoui ◽  
Mohamed Dafir Ech-Chrif El Kettani
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption

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 Verifiable Fully Homomorphic Encryption Scheme for Cloud Computing Security

Technologies ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Ahmed EL-YAHYAOUI ◽  
Mohamed Dafir ECH-CHERIF EL KETTANI
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
Verifiable Computation ◽  
Multiplication Operation ◽  
Matrix Operations ◽  
Encryption Schemes ◽  
Simple Matrix

Performing smart computations in a context of cloud computing and big data is highly appreciated today. It allows customers to fully benefit from cloud computing capacities (such as processing or storage) without losing confidentiality of sensitive data. Fully homomorphic encryption (FHE) is a smart category of encryption schemes that enables working with the data in its encrypted form. It permits us to preserve confidentiality of our sensible data and to benefit from cloud computing capabilities. While FHE is combined with verifiable computation, it offers efficient procedures for outsourcing computations over encrypted data to a remote, but non-trusted, cloud server. The resulting scheme is called Verifiable Fully Homomorphic Encryption (VFHE). Currently, it has been demonstrated by many existing schemes that the theory is feasible but the efficiency needs to be dramatically improved in order to make it usable for real applications. One subtle difficulty is how to efficiently handle the noise. This paper aims to introduce an efficient and symmetric verifiable FHE based on a new mathematic structure that is noise free. In our encryption scheme, the noise is constant and does not depend on homomorphic evaluation of ciphertexts. The homomorphy of our scheme is obtained from simple matrix operations (addition and multiplication). The running time of the multiplication operation of our encryption scheme in a cloud environment has an order of a few milliseconds.

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 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.

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