Security structure of cloud storage based on homomorphic encryption scheme

Li, Jian; Chen, Sicong; Song, Danjie

doi:10.1109/ccis.2012.6664401

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Nevertheless, some encryption algorithms are not applicable for computing ciphertext directly in cloud computing platform. On the contrary, homomorphic encryption can execute operations of ciphertext directly, which called the feature as algebraic homomorphism [16]- [18]. However, as the high computational complexity of homomorphic encryption, many scholars concentrate on how to improve the execution efficiency for sake of the popularity of the cloud computing platform [19].…”

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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“…For each different combination of the values to one attribute group, we use different groups of prime numbers for decryption. If the server wants to decrypt the information, it will need to try 75 times on average (searching in disordered arrangement [32]). It is clear that the time of decryption by the server is almost squarely proportional to the numbers of attributes.…”

Section: Possibility Of Decryptionmentioning

confidence: 99%

Privacy Preservation for Friend-Recommendation Applications

Wang

Huang

2018

Security and Communication Networks

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Friend-recommendation applications as one kind of typical social applications can satisfy the social contact needs of different users and become tools for developing a social relationship. However, the privacy leakage has turned into an insurmountable obstacle to the market success of such applications. Existing privacy protection approaches for social applications either introduce untrusted third parties or sacrifice information accuracy. As for friend-recommendation applications particularly, the multihop trust chain and anonymous message methods still have a defect that the hacker can act as a user to acquire information. In this paper, we put forward the privacy protection mechanism based on zero knowledge without any privacy leakage to the application server. In detail, the server knows nothing about the user’s information, but can still provide users with accurate information on friend recommendation. We also analyze the potential attack methods and propose the corresponding solution. Our simulation results verify the effectivity and efficiency of our scheme.

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“…Focuses on improving the security of two-party k-means while maintaining the correctness of algorithm. K-anonymity [10], noise transformation and multiplicative transformation are some PPDM (privacy preserving data mining) methods. Compared to PPDM secure cloud mining is a relatively newer field.…”

Section: Introductionmentioning

confidence: 99%

Digital Signature to Secure Data in Cloud Using Homomorphic Encryption Technique

2016

IJAERD

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Security structure of cloud storage based on homomorphic encryption scheme

Cited by 11 publications

References 2 publications

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud

Privacy Preservation for Friend-Recommendation Applications

Digital Signature to Secure Data in Cloud Using Homomorphic Encryption Technique

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