scholarly journals A Comparative Study of Homomorphic Encryption Schemes Using Microsoft SEAL

2021 ◽  
Vol 2128 (1) ◽  
pp. 012021
Author(s):  
Shereen Mohamed Fawaz ◽  
Nahla Belal ◽  
Adel ElRefaey ◽  
Mohamed Waleed Fakhr
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Comparative Study ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Arithmetic Operations ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Encryption Schemes ◽  
The Difference

Abstract Fully homomorphic encryption (FHE) technology is a method of encrypting data that allows arbitrary calculations to be computed. Machine learning (ML) and many other applications are relevant to FHE such as Cloud Computing, Secure Multi-Party, and Data Aggregation. Only the authenticated user has the authority to decrypt the ciphertext and understand its meaning, as encrypted data can be computed and processed to produce an encrypted output. Homomorphic encryption uses arithmetic circuits that focus on addition and multiplication, allowing the user to add and multiply integers while encrypted. This paper discusses the performance of the Brakerski-Fan-Vercauteren scheme (BFV) and Cheon, Kim, Kim, and Song (CKKS) scheme using one of the most important libraries of FHE “Microsoft SEAL”, by applying certain arithmetic operations and observing the time consumed for every function applied in each scheme and the noise budget after every operation. The results obtained show the difference between the two schemes when applying the same operation and the number of sequential operations each can handle.

About Fully Homomorphic Encryption Improvement Techniques

2019 ◽  
Vol 10 (3) ◽  
pp. 1-20
Author(s):  
Ahmed El-Yahyaoui ◽  
Mohamed Daifr Ech-Cherif El Kettani
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Quality Of Services ◽  
End User ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Cloud Server ◽  
Encryption Schemes ◽  
Multiple Clients

Fully homomorphic encryption schemes (FHE) are a type of encryption algorithm dedicated to data security in cloud computing. It allows for performing computations over ciphertext. In addition to this characteristic, a verifiable FHE scheme has the capacity to allow an end user to verify the correctness of the computations done by a cloud server on his encrypted data. Since FHE schemes are known to be greedy in term of processing consumption and slow in terms of runtime execution, it is very useful to look for improvement techniques and tools to improve FHE performance. Parallelizing computations is among the best tools one can use for FHE improvement. Batching is a kind of parallelization of computations when applied to an FHE scheme, it gives it the capacity of encrypting and homomorphically processing a vector of plaintexts as a single ciphertext. This is used in the context of cloud computing to perform a known function on several ciphertexts for multiple clients at the same time. The advantage here is in optimizing resources on the cloud side and improving the quality of services provided by the cloud computing. In this article, the authors will present a detailed survey of different FHE improvement techniques in the literature and apply the batching technique to a promising verifiable FHE (VFHE) recently presented by the authors at the WINCOM17 conference.

About Fully Homomorphic Encryption Improvement Techniques

2021 ◽  
pp. 1331-1351
Author(s):  
Ahmed El-Yahyaoui ◽  
Mohamed Daifr Ech-Cherif El Kettani
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Encryption Algorithm ◽  
End User ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Cloud Server ◽  
Encryption Schemes ◽  
Multiple Clients

Fully homomorphic encryption schemes (FHE) are a type of encryption algorithm dedicated to data security in cloud computing. It allows for performing computations over ciphertext. In addition to this characteristic, a verifiable FHE scheme has the capacity to allow an end user to verify the correctness of the computations done by a cloud server on his encrypted data. Since FHE schemes are known to be greedy in term of processing consumption and slow in terms of runtime execution, it is very useful to look for improvement techniques and tools to improve FHE performance. Parallelizing computations is among the best tools one can use for FHE improvement. Batching is a kind of parallelization of computations when applied to an FHE scheme, it gives it the capacity of encrypting and homomorphically processing a vector of plaintexts as a single ciphertext. This is used in the context of cloud computing to perform a known function on several ciphertexts for multiple clients at the same time. The advantage here is in optimizing resources on the cloud side and improving the quality of services provided by the cloud computing. In this article, the authors will present a detailed survey of different FHE improvement techniques in the literature and apply the batching technique to a promising verifiable FHE (VFHE) recently presented by the authors at the WINCOM17 conference.

scholarly journals A Proposed Fully Homomorphic for Securing Cloud Banking Data at Rest

2020 ◽  
Vol 4 (1) ◽  
pp. 87
Author(s):  
Zana Thalage Omar ◽  
Fadhil Salman Abed
Keyword(s):  
Homomorphic Encryption ◽  
Prime Numbers ◽  
Data Encryption ◽  
Theoretical Research ◽  
Significant Progress ◽  
Arithmetic Operations ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data ◽  
Encryption And Decryption ◽  
Local Cloud

Fully homomorphic encryption (FHE) reaped the importance and amazement of most researchers and followers in data encryption issues, as programs are allowed to perform arithmetic operations on encrypted data without decrypting it and obtain results similar to the effects of arithmetic operations on unencrypted data. The first (FHE) model was introduced by Craig Gentry in 2009, and it was just theoretical research, but later significant progress was made on it, this research offers FHE system based on directly of factoring big prime numbers which consider open problem now, The proposed scheme offers a fully homomorphic system for data encryption and stores it in encrypted form on the cloud based on a new algorithm that has been tried on a local cloud and compared with two previous encryption systems (RSA and Paillier) and shows us that this algorithm reduces the time of encryption and decryption by 5 times compared to other systems.

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 Modified Fully Homomorphic Encryption based on Parallel Processing in Cloud Computing

2021 ◽  
pp. 250-262
Author(s):  
Parth Tandel ◽  
Abhinav Shubhrant ◽  
Mayank Sohani
Keyword(s):  
Cloud Computing ◽  
Parallel Processing ◽  
Data Security ◽  
Privacy Protection ◽  
Homomorphic Encryption ◽  
Security And Privacy ◽  
Fully Homomorphic Encryption ◽  
Privacy And Security ◽  
Encrypted Data ◽  
Encryption Algorithms

Cloud Computing is widely regarded as the most radically altering trend in information technology. However, great benefits come with great challenges, especially in the area of data security and privacy protection. Since standard cloud computing uses plaintext, certain encryption algorithms were implemented in the cloud for security reasons, and ‘encrypted' data was then stored in the cloud. Homomorphic Encryption (HE), a modern kind of encryption strategy, is born as a result of this change. Primarily, the paper will focus on implementing a successful Homomorphic Encryption (HE) scheme for polynomials. Furthermore, the objective of the paper is to propose, produce and implement a method to convert the already implemented sequentially processing Homomorphic Encryption into parallel processing Homomorphic Encryption (HE) using a Parallel Processing concept (Partitioning, Assigning, Scheduling, etc) and thereby producing a better performing Homomorphic Encryption (HE) called Fully Homomorphic Encryption (FHE). Fully Homomorphic Encryption (FHE) is an encryption technique that can perform specific analytical operations, functions and methods on normal or encrypted data and can still perform traditional encryption results as performed on plaintext. The three major reasons for implementing Fully Homomorphic Encryption (FHE) are advantages like no involvement of third parties, trade-off elimination between privacy and security and quantum safety.

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
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