A Key-Ordered Decisional Learning Parity with Noise (DLPN) Scheme for Public Key Encryption Scheme in Cloud Computing

Lakum, Tarasvi; Rao, B. Thirumala

doi:10.14569/ijacsa.2019.0101121

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Security against CCA [12] gives the exact security representation for a cryptosystem. The literature works [13]- [17] have come up with the schemes to make a secure cryptosystem through the semantic security systems.…”

Section: Related Workmentioning

confidence: 99%

Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment

Lakum

Rao²

2022

IJECE

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<p><span>In this paper, we are proposing a mutual query data sharing protocol (MQDS) to overcome the encryption or decryption time limitations of exiting protocols like Boneh, rivest shamir adleman (RSA), Multi-bit transposed ring learning parity with noise (TRLPN), ring learning parity with noise (Ring-LPN) cryptosystem, key-Ordered decisional learning parity with noise (kO-DLPN), and KD_CS protocol’s. Titled scheme is to provide the security for the authenticated user data among the distributed physical users and devices. The proposed data sharing protocol is designed to resist the chosen-ciphertext attack (CCA) under the hardness solution for the query shared-strong diffie-hellman (SDH) problem. The evaluation of proposed work with the existing data sharing protocols in computational and communication overhead through their response time is evaluated.</span></p>

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Section: Related Workmentioning

confidence: 99%

Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment

Lakum

Rao²

2022

IJECE

Self Cite

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“…Frequency [25],different decisional learning parity with noise (DLPN) named as key-Ordered DLPN based security algorithm is proposed, here DLPN is extended to an evenodd-order mechanism for the encryption, in this, odd and even bits are input integer values for the key generation algorithm in that security method.…”

Section: Data Classification Properties Access Control Content Storagementioning

confidence: 99%

Enhanced Data security through Deep Data Classification in the Cloud Computing

2020

IJETER

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Data is a valued asset especially when data moving to the cloud. Cloud computing model attracts different users because of its various benefits such as scalability, simplicity, expense reduction, and high resource resistance. Cloud computing is a wide-ranging architecture based on different models for providing different software services and hardware services. There may be different types of data and the degree of protection needed for all the data is also may vary. Data privacy and data security is the major area of research and development in the cloud computing. Privacy protection and data leakage became essential thing for so many organizations moving on to cloud. Several classification methods that are expressed various parameters based on different dimensions for providing the data security can be based on the level and are required protection. But, the latest approaches introduced by the cloud, related to distributed resources, multi-tenancy idea, computation outsourcing, high dynamism of the model, data warehousing and the non-transparent style of cloud increase the security and privacy concerns and makes building and maintaining trust among cloud service providers and consumers is a serious security issue. In this paper, we proposes a new approach to improve security of data in cloud computing. It proposes a classification model to classify data before being presented into a suitable encryption system based on the category. Because data in cloud has not the same sensitivity level, encrypting these different types of data with the same algorithms can lead to a lack of security or resources. By this scheme, we can achieve high security for our data, and can optimize the computation cost and resource consumption while ensuring data confidentiality.The efficiency of the proposed classification scheme is analysed with the sample dataset collected.

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A Key-Ordered Decisional Learning Parity with Noise (DLPN) Scheme for Public Key Encryption Scheme in Cloud Computing

Cited by 2 publications

References 23 publications

Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment

Mutual query data sharing protocol for public key encryption through chosen-ciphertext attack in cloud environment

Enhanced Data security through Deep Data Classification in the Cloud Computing

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