Abstract
Frequent itemset mining and Association Rule Mining are the extensively utilized data analysis techniques for a transactional database concerned with a trade-off. Data owners wish to acquire knowledge in these data analysis techniques to protect their sensitive data from additional data proprietor and third parties. This work emphasizes on privacy-preserving frequent itemset mining on a vertically partitioned database. An efficient homomorphic encryption scheme is designed to assure data privacy. Cryptography is a part of encryption that is used to guard information against third parties. In this work, frequent itemset mining algorithms such as Eclat, apriori, and FP-Growth are taken for analysis in terms of computation time and scalability of data. The analysis result shows that apriori algorithm is less time-consuming to generate rule in the cloud, irrespective of the number of transactions.
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Yogasini, M., Prathibha, B.N. (2022). Comparative Analysis on Frequent Itemset Mining Algorithms in Vertically Partitioned Cloud Data. In: Sivasubramanian, A., Shastry, P.N., Hong, P.C. (eds) Futuristic Communication and Network Technologies. VICFCNT 2020. Lecture Notes in Electrical Engineering, vol 792. Springer, Singapore. https://doi.org/10.1007/978-981-16-4625-6_38
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DOI: https://doi.org/10.1007/978-981-16-4625-6_38
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