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The rapid growth in the database data led to create of a large amount of data and the problem to access this data for answering user queries becomes more challenges.This work is concerned with problem of data retrieving from big encrypted database by proposing an approach consists(datamanagement, Encryption, DNA computing and Retrieval). The ICM algorithm and the three proposed clustering algorithm can be applied for big databases in both status (plain and encrypted) form.The second stage presents amodified CAST encryption algorithm to encrypt the database files (original or summarized).The S-Box that is used in the modified CAST algorithm is constructed by using an intelligent technique (DNA computing).Also, modified keys were used; the keys are generated by using AES generator with simple modification. After encryption process, the clusters can be built dynamicallyaccording to the user query byapplying suggested dynamic clustering algorithm which is used for both plain and encrypted data; this process provides time for the retrieval process because the files that are resulted from applying dynamic clustering algorithm include only relevant data to the user query.
| Autorius: | Rasha Subhi Ali, Ghassan Hameed Abdul Majeed, Alaa Kadhim Farhan, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2018 |
| Knygos puslapių skaičius: | 176 |
| ISBN-10: | 6137332934 |
| ISBN-13: | 9786137332931 |
| Formatas: | 220 x 150 x 11 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Time Optimization of Encrypted QueryRetrieval for Intelligent Big Data“
