Cooperative Methodology to Generate a New Scheme for Cryptography
Paper ID : 1170-ICTCK
Samaher Al_Janabi *
University of Babylon
In this paper, a novel method named as Frequency Pattern-Knowledge Constructions is developed. This method attempts to develop Frequency Pattern Growth data mining algorithm using several knowledge constructions to find the association rules and minimize the shared information ( i.e. fined frequent item set), FP-KC combines the criteria of Principal Component Analysis with FP-Growth techniques. These criteria include eigenvalues, cumulative variability and scree plot. There are several reasons for developing the FP-Growth data mining algorithm to build up a novel FP-KC technique that can find the association rules, including: (a) the size of an FP-tree is typically smaller than the size of the uncompressed data because many records in a dataset often have a few items (b) to give the best result in the case that all the records have the same set of items; (c) FP-Growth is an efficient algorithm because it illustrates how a compact representation of the transaction dataset helps to efficiently generate frequent item sets; and (d) The run-time performance of FP-Growth depends on the compaction factor of the dataset, while the enhanced algorithm in Subliminal Channel depends on both the position of a character in the alphabet and its position in the plain rule word , with a specific function to determine the cipher rule character. To evaluate the efficiency of the proposed method, four case studies were used. Based on the results, the proposed method can be considered as an efficient technique for secure mining of association rules of partitioned data.
Data Mining – Subliminal Cryptography – Association Rules – Knowledge Constructions – Principle Component Analysis
Status : Paper Accepted