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Relevant bibliographies by topics / RULE HIDING

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Journal articles

Academic literature on the topic 'RULE HIDING'

Author: Grafiati

Published: 11 September 2023

Last updated: 16 April 2024

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Journal articles on the topic "RULE HIDING"

1

Verykios, V. S., A. K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni. "Association rule hiding." IEEE Transactions on Knowledge and Data Engineering 16, no. 4 (2004): 434–47. http://dx.doi.org/10.1109/tkde.2004.1269668.

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2

Khurana, Garvit. "Association Rule Hiding using Hash Tree." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 787–89. http://dx.doi.org/10.31142/ijtsrd23037.

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3

Quoc Le, Hai, Somjit Arch-int, and Ngamnij Arch-int. "Association Rule Hiding Based on Intersection Lattice." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/210405.

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Association rule hiding has been playing a vital role in sensitive knowledge preservation when sharing data between enterprises. The aim of association rule hiding is to remove sensitive association rules from the released database such that side effects are reduced as low as possible. This research proposes an efficient algorithm for hiding a specified set of sensitive association rules based on intersection lattice of frequent itemsets. In this research, we begin by analyzing the theory of the intersection lattice of frequent itemsets and the applicability of this theory into association rul

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4

Wang, Hui. "Hiding Sensitive Association Rules by Sanitizing." Advanced Materials Research 694-697 (May 2013): 2317–21. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2317.

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The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.

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5

Mohan, S. Vijayarani, and Tamilarasi Angamuthu. "Association Rule Hiding in Privacy Preserving Data Mining." International Journal of Information Security and Privacy 12, no. 3 (2018): 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.

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This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing informa

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6

Verykios, Vassilios S. "Association rule hiding methods." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 1 (2013): 28–36. http://dx.doi.org/10.1002/widm.1082.

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7

Wang, Hui. "Strategies for Sensitive Association Rule Hiding." Applied Mechanics and Materials 336-338 (July 2013): 2203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2203.

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Data mining technologies are used widely while the side effects it incurred are concerned so seriously. Privacy preserving data mining is so important for data and knowledge security during data mining applications. Association rule extracted from data mining is one kind of the most popular knowledge. It is challenging to hide sensitive association rules extracted by data mining process and make less affection on non-sensitive rules and the original database. In this work, we focus on specific association rule automatic hiding. Novel strategies are proposed which are based on increasing the su

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8

Öztürk, Ahmet Cumhur, and Belgin Ergenç. "Dynamic Itemset Hiding Algorithm for Multiple Sensitive Support Thresholds." International Journal of Data Warehousing and Mining 14, no. 2 (2018): 37–59. http://dx.doi.org/10.4018/ijdwm.2018040103.

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This article describes how association rule mining is used for extracting relations between items in transactional databases and is beneficial for decision-making. However, association rule mining can pose a threat to the privacy of the knowledge when the data is shared without hiding the confidential association rules of the data owner. One of the ways hiding an association rule from the database is to conceal the itemsets (co-occurring items) from which the sensitive association rules are generated. These sensitive itemsets are sanitized by the itemset hiding processes. Most of the existing

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9

Wang, Shyue-Liang, Bhavesh Parikh, and Ayat Jafari. "Hiding informative association rule sets." Expert Systems with Applications 33, no. 2 (2007): 316–23. http://dx.doi.org/10.1016/j.eswa.2006.05.022.

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10

B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this pa

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