scholarly journals Homomorphic Encryption Using Enhanced BGV Encryption Scheme For Cloud Security

2018 ◽  
Vol 7 (03) ◽  
pp. 23785-23789
Author(s):  
S.V.Suriya Prasad ◽  
K. Kumanan
Keyword(s):  
Homomorphic Encryption ◽  
Cloud Service ◽  
Cloud Security ◽  
Encryption Scheme ◽  
Public Cloud ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Or Applications

Fully Homomorphic Encryption is used to enhance the security incase of un-trusted systems or applications that deals with sensitive data. Homomorphic encryption enables computation on encrypted data without decryption. Homomorphic encryption prevents sharing of data within the cloud service where data is stored in a public cloud . In Partially Homomorphic Encryption it performs either additive or multiplicative operation, but not both operation can be carried out at a same time. Whereas , in case of Fully Homomorphic Encryption both operations can be carried out at same time. In this model , Enhanced BGV Encryption Technique is used to perform FHE operations on encrypted data and sorting is performed using the encrypted data

An Efficient Fully Homomorphic Encryption Scheme for Private Information Retrieval in the Cloud

2019 ◽  
Vol 34 (04) ◽  
pp. 2055008
Author(s):  
Xun Wang ◽  
Tao Luo ◽  
Jianfeng Li
Keyword(s):  
Information Retrieval ◽  
Theoretical Analysis ◽  
Private Information ◽  
Homomorphic Encryption ◽  
Private Information Retrieval ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Privacy Concerns ◽  
Simulation Results

Information retrieval in the cloud is common and convenient. Nevertheless, privacy concerns should not be ignored as the cloud is not fully trustable. Fully Homomorphic Encryption (FHE) allows arbitrary operations to be performed on encrypted data, where the decryption of the result of ciphertext operation equals that of the corresponding plaintext operation. Thus, FHE schemes can be utilized for private information retrieval (PIR) on encrypted data. In the FHE scheme proposed by Ducas and Micciancio (DM), only a single homomorphic NOT AND (NAND) operation is allowed between consecutive ciphertext refreshings. Aiming at this problem, an improved FHE scheme is proposed for efficient PIR where homomorphic additions and multiplications are based on linear operations on ciphertext vectors. Theoretical analysis shows that when compared with the DM scheme, the proposed scheme allows multiple homomorphic additions and a single homomorphic multiplication to be performed. The number of allowed homomorphic additions is determined by the ratio of the ciphertext modulus to the upper bound of initial ciphertext noise. Moreover, simulation results show that the proposed scheme is significantly faster than the DM scheme in the homomorphic evaluation for a series of algorithms.

scholarly journals Oblivious Lookup-Tables

2016 ◽  
Vol 67 (1) ◽  
pp. 191-203
Author(s):  
Markus Stefan Wamser ◽  
Stefan Rass ◽  
Peter Schartner
Keyword(s):  
Medical Records ◽  
Homomorphic Encryption ◽  
Input Parameter ◽  
Software As A Service ◽  
Fully Homomorphic Encryption ◽  
Sensitive Data ◽  
New Approach ◽  
Encrypted Data ◽  
Practical Applications ◽  
Current State

Abstract Evaluating arbitrary functions on encrypted data is one of the holy grails of cryptography, with Fully Homomorphic Encryption (FHE) being probably the most prominent and powerful example. FHE, in its current state is, however, not efficient enough for practical applications. On the other hand, simple homomorphic and somewhat homomorphic approaches are not powerful enough to support arbitrary computations. We propose a new approach towards a practicable system for evaluating functions on encrypted data. Our approach allows to chain an arbitrary number of computations, which makes it more powerful than existing efficient schemes. As with basic FHE we do not encrypt or in any way hide the function, that is evaluated on the encrypted data. It is, however, sufficient that the function description is known only to the evaluator. This situation arises in practice for software as a Software as a Service (SaaS)-scenarios, where an evaluator provides a function only known to him and the user wants to protect his data. Another application might be the analysis of sensitive data, such as medical records. In this paper we restrict ourselves to functions with only one input parameter, which allow arbitrary transformations on encrypted data.

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.

scholarly journals t-BMPNet: Trainable Bitwise Multilayer Perceptron Neural Network over Fully Homomorphic Encryption Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Joon Soo Yoo ◽  
Ji Won Yoon
Keyword(s):  
Neural Network ◽  
Homomorphic Encryption ◽  
Activation Function ◽  
Significant Error ◽  
Security And Privacy ◽  
Encryption Scheme ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Lattice Problem ◽  
High Level

Homomorphic encryption (HE) is notable for enabling computation on encrypted data as well as guaranteeing high-level security based on the hardness of the lattice problem. In this sense, the advantage of HE has facilitated research that can perform data analysis in an encrypted state as a purpose of achieving security and privacy for both clients and the cloud. However, much of the literature is centered around building a network that only provides an encrypted prediction result rather than constructing a system that can learn from the encrypted data to provide more accurate answers for the clients. Moreover, their research uses simple polynomial approximations to design an activation function causing a possibly significant error in prediction results. Conversely, our approach is more fundamental; we present t-BMPNet which is a neural network over fully homomorphic encryption scheme that is built upon primitive gates and fundamental bitwise homomorphic operations. Thus, our model can tackle the nonlinearity problem of approximating the activation function in a more sophisticated way. Moreover, we show that our t-BMPNet can perform training—backpropagation and feedforward algorithms—in the encrypted domain, unlike other literature. Last, we apply our approach to a small dataset to demonstrate the feasibility of our model.

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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