A novel implementation of parallel homomorphic encryption for secure data storage in cloud

Sethi, Kamalakanta; Majumdar, Amartya; Bera, Padmalochan

doi:10.1109/cybersecpods.2017.8074851

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…Lately, scholars have researched many parallel computing modes in homomorphic encryption [21]- [23]. Sethi et al [24] proposed a model to parallel homomorphic encryption based on MapReduce framework. Similarly, Zhaoe Min et al [25] proposed another parallel homomorphic encryption scheme based on MapReduce environment with 16 cores and 4 nodes.…”

Section: Related Workmentioning

confidence: 99%

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Xia¹

2019

JCP

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As an important encryption algorithm of performing computations directly on ciphertext without any need of decryption and compromising privacy, homomorphic encryption is an increasingly popular research topic of protecting privacy of the data in cloud security research. However, it will be a heavy workload for resources as the high computational complexity of homomorphic encryption. Therefore, GPU acceleration is employed to speed up homomorphic encryption. Motivated by this observation, we utilize parallel computing mode in DGHV algorithm with GPUs acceleration based on CPU-GPUs hybrid system. Our main contribution is to present a parallel computing mode for large-scale data encryption based on CPU-GPUs hybrid system as fast as possible. Specifically, we applies parallel computing mode in DGHV with GPUs acceleration to reduce the time duration and provide a comparative performance study. We further design pipeline architecture of processing stream to accelerate the speed of DGHV algorithm. Furthermore, experimental results validate that different parallelism has the corresponding granularity in parallel computing mode. Experimental results show that our method gains more than 84% improvement (run time), 67% improvement (run time), and 80% improvement (run time) compared to the sequential data encryption, sequential homomorphic addition, and sequential homomorphic multiplication in DGHV algorithm respectively.

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Section: Related Workmentioning

confidence: 99%

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Xia¹

2019

JCP

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“…The privacy protection scheme to be designed should be able to realise the transparent transmission of the other nodes and links by target node data when the attacker intercepts some nodes in WSN and even monitors the whole network (Chen et al, 2015;Sethi et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Additive Aggregate Function-based Data Privacy Protection Algorithm

Chen

Lee

2019

IJAITG

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Data privacy protection is a key problem in the research and application of the internet of things that in wireless sensor networks has broad application prospects, such as agriculture environmental monitoring, healthcare, etc. For the core part of the internet of things, wireless sensor network, it is a very challenging task to provide effective privacy protection in the process of data transmission. In this paper, a new type of data privacy protection strategy based on the additive aggregation function is proposed (hereafter referred to as DPPA). On the one hand, applying the superposition property of the node data, the paper introduces the class aggregation protocol. On the other hand, taking advantage of the algebraic properties of the polynomials and the correlation properties of the superposed data, it can reduce the data of the header files of the data packages, and reduce the data size, while improving the privacy preservation. Simulation results show that DPPA can effectively protect data privacy, and get accurate data fusion results, while reducing the amount of data traffic.

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Implementation of High Throughput XTS-SM4 Module for Data Storage Devices

Liang

Liu

et al. 2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A novel implementation of parallel homomorphic encryption for secure data storage in cloud

Cited by 9 publications

References 8 publications

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Additive Aggregate Function-based Data Privacy Protection Algorithm

Implementation of High Throughput XTS-SM4 Module for Data Storage Devices

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