New Fully Homomorphic Encryption Scheme Based on Multistage Partial Homomorphic Encryption Applied in Cloud Computing

Mahmood, Zainab H.; Ibrahem, Mahmood Khalel

doi:10.1109/aicis.2018.00043

Cited by 42 publications

(16 citation statements)

References 7 publications

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“…At present, there are many fully homomorphic encryption (FHE) algorithms. Gentry for the first time proposed a homomorphic encryption algorithm that can carry out both multiplication and addition operations, but its implementation in practical applications is low [4]. A BGV scheme designed a new homomorphic encryption construction technology, and the two schemes are described in detail as follows.…”

Section: Methodsmentioning

confidence: 99%

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Huang¹,

Wu²

2022

Security and Communication Networks

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Blockchain is a distributed time-series database. Based on the blockchain platform, this paper designs the framework model of cloud storage and designs the cloud storage model based on homomorphic encryption based on the operation process of the model. According to the applicability of underlying blockchain storage, the HElib library is established as the algorithm for data privacy protection. The BGV homomorphic encryption algorithm is used as the bottom layer of the algorithm, and the efficiency of the BGV homomorphic encryption algorithm is compared with that of the Gentry’s bootstrap-based homomorphic encryption algorithm. It is proved that the BGV algorithm is more suitable for big data.

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Section: Methodsmentioning

confidence: 99%

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Huang¹,

Wu²

2022

Security and Communication Networks

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“…One is to use Trusted Execution Environments (TEEs) [19] on all nodes to protect the model data. The other way is to use homomorphic encryption [20] to encrypt the model, which allows other nodes to use the model without decrypting it.…”

Section: Model Duplication Attacksmentioning

confidence: 99%

DFL: High-Performance Blockchain-Based Federated Learning

Tian¹,

Guo²,

Zhang³

et al. 2021

Preprint

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Many researchers are trying to replace the aggregation server in federated learning with a blockchain system to achieve better privacy, robustness and scalability. In this case, clients will upload their updated models to the blockchain ledger, and use a smart contract on the blockchain system to perform model averaging. However, running machine learning applications on the blockchain is almost impossible because a blockchain system, which usually takes over half minute to generate a block, is extremely slow and unable to support machine learning applications.This paper proposes a completely new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. This architecture inherits most traditional blockchain merits and achieves extremely high performance with low resource consumption by waiving global consensus. To characterize the performance and robustness of our architecture, we implement the architecture as a prototype and test it on a physical four-node network. To test more nodes and more complex situations, we build a simulator to simulate the network. The LeNet results indicate our system can reach over 90% accuracy for non-I.I.D. datasets even while facing model poisoning attacks, with the blockchain consuming less than 5% of hardware resources.

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“…In addition, environments where speed is paramount, for instance, big data or real-time are also affected by encryption as the client cannot run queries on encrypted data [23]. Even with the existence of homomorphic encryption, which allows encrypted data to be processed, the large key size and low calculation efficiency, hinders its practicality in cloud computing [24] [25].…”

Section: B Encryptionmentioning

confidence: 99%

Enhancing Data Security in Cloud using Random Pattern Fragmentation and a Distributed NoSQL Database

Santos

Ghita

Masala

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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The cloud computing model has become very popular among users, as it has proven to be a cost-effective solution to store and process data, thanks to recent advancements in virtualization and distributed computing. Nevertheless, in the cloud environment, the user entrusts the safekeeping of its data entirely to the provider, which introduces the problem of how secure such data is and whether its integrity has been maintained. This paper proposes an approach to the data security in cloud by utilizing a random pattern fragmentation algorithm and combine it with a distributed NoSQL database. This not only increases the security of the data by storing it in different nodes and scramble all the bytes, but also allows the user to implement an alternative method of securing data. The performance of the approach is compared to other approaches, along with AES 256 encryption. Results indicate a significant performance improvement over encryption, highlighting the capabilities of this method in cloud, as it creates a layer of protection without additional overhead.

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New Fully Homomorphic Encryption Scheme Based on Multistage Partial Homomorphic Encryption Applied in Cloud Computing

Cited by 42 publications

References 7 publications

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

Cloud Storage Model Based on the BGV Fully Homomorphic Encryption in the Blockchain Environment

DFL: High-Performance Blockchain-Based Federated Learning

Enhancing Data Security in Cloud using Random Pattern Fragmentation and a Distributed NoSQL Database

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