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- 저자 : Narayana K. E. (검색결과 1 건)
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Narayana K. E.,Jayashree K. 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3
Towards efcient data storage and maintenance in cloud, diferent privacy preservation and data deduplication schemes are presented in literature. However, the methods sufer to mitigate side channel attacks in cloud which challenges the data security. To handle this, an efcient Class based Dynamic Feature Centric Data De-duplication (CDFC-DD) is presented in this article. The method classifes the data and features under several classes according to their sensitivity. In this way, the method enforces efective access restriction scheme in class level using Class based Access Restriction Scheme (CARS) with the use of client profle. The method would compute the class level trust measure (CLTM) based on which access restriction is performed. Similarly, the method maintains a feature matrix, which contains the class of feature and indexes belongs to the feature, scheme and key. By computing Legitimate Access Measure (LAM) value, the method performs data deduplication. Also, the data has been involved in two way data encryption which encrypts the data with the feature key by the user where it has been re-encrypted using class key to improve the security performance. The proposed CDFC-DD scheme improves data security and improves the QoS of the environment. The Proposed method fnds the data identifying a specifc owner, and validation ensures that authenticated data at least corresponds exactly to a set of users in cloud storage. Simulation results show the Access restriction (AR), Encryption/Decryption Performance and Time Complexity (T) for increasing performance based on the CDFC-DD better than the existing method.
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