Relevant bibliographies by topics / ASSOCIATION RULE HIDING

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'ASSOCIATION RULE HIDING'

Author: Grafiati
Published: 11 September 2023
Last updated: 26 July 2025

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Journal articles on the topic "ASSOCIATION RULE HIDING"

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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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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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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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Tata, Gayathri, and Durga N. "Privacy Preserving Approaches for High Dimensional Data." International Journal of Trend in Scientific Research and Development 1, no. 5 (2017): 1120–25. https://doi.org/10.31142/ijtsrd2430.

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This paper proposes a model for hiding sensitive association rules for Privacy preserving in high dimensional data. Privacy preservation is a big challenge in data mining. The protection of sensitive information becomes a critical issue when releasing data to outside parties. Association rule mining could be very useful in such situations. It could be used to identify all the possible ways by which 'non confidential' data can reveal 'confidential' data, which is commonly known as 'inference problem'. This issue is solved using Association Rule Hiding ARH techniques in P
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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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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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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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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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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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Garvit, Khurana. "Association Rule Hiding using Hash Tree." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 787–89. https://doi.org/10.31142/ijtsrd23037.

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As extensive chronicles of information contain classified rules that must be protected before distributed, association rule hiding winds up one of basic privacy preserving data mining issues. Information sharing between two associations is ordinary in various application zones for instance business planning or marketing. Profitable overall patterns can be found from the incorporated dataset. In any case, some delicate patterns that ought to have been kept private could likewise be uncovered. Vast disclosure of touchy patterns could diminish the forceful limit of the information owner. Database
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Dissertations / Theses on the topic "ASSOCIATION RULE HIDING"

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LaMacchia, Carolyn. "Improving the Scalability of an Exact Approach for Frequent Item Set Hiding." NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/205.

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Technological advances have led to the generation of large databases of organizational data recognized as an information-rich, strategic asset for internal analysis and sharing with trading partners. Data mining techniques can discover patterns in large databases including relationships considered strategically relevant to the owner of the data. The frequent item set hiding problem is an area of active research to study approaches for hiding the sensitive knowledge patterns before disclosing the data outside the organization. Several methods address hiding sensitive item sets including an exac
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VARSHNEY, PEEYUSH. "CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16143.

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Data mining process are followed an extensive undertaking of research and product improvement. This development started when enterprise material was first loaded on computers, continued with advancement in data access, and more recently, developed technologies that permit users to transport through their data in real time. APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. In this report, I study the data structure
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VARSHNEY, PEEYUSH. "CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING." Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16318.

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Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. In this report, I examine the data structure, implement
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Saikia, Bikramjit, and Debkumar Bhowmik. "Study of Association Rule Mining and Different Hiding Techniques." Thesis, 2009. http://ethesis.nitrkl.ac.in/991/1/Thesis.pdf.

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Data mining is the process of extracting hidden patterns from data. As more data is gathered,with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. In this paper, we first focused on APRIORI algorithm, a popular data mining technique and compared the performances of a linked list based implementation as a basis and a tries-based implementation on it for mining frequent item sequences in a transactional database. We examined the data structure, implementation and algorithmic features mainly focusing o
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Chan, Ching-yi, and 詹景逸. "A Study for Association Rule Hiding Using the Evaluation of Side-Effec." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/44956271738886770834.

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碩士<br>國立臺南大學<br>資訊教育研究所碩士班<br>93<br>Data mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. There are some algorithm removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. However it causes another problem "the side effect" that is a NP-Hard problem proofed by Atallah. We introduce a new perspective where is "Side Effect Cost Evaluation" to solve this problem. The efficacy and time
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Kao, Tai-wei, and 高黛威. "Hiding dynamic sensitive association rules in incremental data." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/29229249691498416855.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>101<br>As the advancement of technologies as well as the intense competition of business, the issues of privacy have acquiring more attention. Mining association rule is the significant technique in data mining. However, it may cause some privacy problem in mining processes. Many researches, thus, start to hide sensitive association rules due to avoid the sensitive information exposed. However, the development of computers and Internet technologies is so fast that data are increasing successively. In addition, sensitive association rules will change with time and pol
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Lai, Ting-Zheng, and 賴廷政. "A Study of Hiding Collaborative Recommendation Association Rules on Horizontally Partitioned Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/19596225694306918568.

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碩士<br>義守大學<br>資訊管理學系碩士班<br>98<br>The study of privacy preserving data mining has become more important in recent years due to the increasing amount of personal data in public, the increasing sophistication of data mining algorithms to leverage this information, and the increasing concern of privacy breaches. Association rule hiding in which some of the association rules are suppressed in order to preserve privacy has been identified as a practical privacy preserving application. Most current association rule hiding techniques assume that the data to be sanitized are in one single data set. How
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Books on the topic "ASSOCIATION RULE HIDING"

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1.

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Gkoulalas-Divanis, Aris. Association rule hiding for data mining. Springer, 2010.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer, 2012.

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Book chapters on the topic "ASSOCIATION RULE HIDING"

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. "Classes of Association Rule Hiding Methodologies." In Advances in Database Systems. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1_3.

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Sharmila, S., and S. Vijayarani. "Association Rule Hiding Using Firefly Optimization Algorithm." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16660-1_68.

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Gopalan, N. P., and T. Satyanarayana Murthy. "Association Rule Hiding Using Chemical Reaction Optimization." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_19.

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Mogtaba, Shyma, and Eiman Kambal. "Association Rule Hiding for Privacy Preserving Data Mining." In Advances in Data Mining. Applications and Theoretical Aspects. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_24.

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Verykios, Vassilios S., and Aris Gkoulalas-Divanis. "A Survey of Association Rule Hiding Methods for Privacy." In Privacy-Preserving Data Mining. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5_11.

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Jagtap, Nitin, and Krishankant P. Adhiya. "Data sanitisation techniques for transactional datasets using association rule hiding techniques." In Recent Advances in Material, Manufacturing, and Machine Learning. CRC Press, 2023. http://dx.doi.org/10.1201/9781003370628-47.

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You, Na Young, Kwang Sun Ryu, Jae Ho Kim, et al. "Association Rule Mining Method to Predict Coronary Artery Disease: KNHANES 2016–2018." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_34.

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Zheng, Huilin, Hyun Woo Park, and Keun Ho Ryu. "An Efficient Association Rule Mining Method to Predict Diabetes Mellitus: KNHANES 2013–2015." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9714-1_26.

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Zhu, Jianming, and Zhanyu Li. "Privacy Preserving Association Rule Mining Algorithm Based on Hybrid Partial Hiding Strategy." In LISS 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40660-7_160.

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Audichya, Dinesh, Prashant Sharma, and Pankaj Kumar Vaishnav. "Determination of Avalanche Effect to Compute the Efficiency of Association Rule Hiding Algorithms." In Artificial Intelligence and Sustainable Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1653-3_55.

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Conference papers on the topic "ASSOCIATION RULE HIDING"

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Zhu, Zutao, and Wenliang Du. "K-anonymous association rule hiding." In the 5th ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1755688.1755726.

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Fovino, Igor Nai, and Alberto Trombetta. "Information driven association rule hiding algorithms." In 2008 1st International Conference on Information Technology (IT 2008). IEEE, 2008. http://dx.doi.org/10.1109/inftech.2008.4621664.

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Zhang, Xiaoming, and Xi Qiao. "New Approach for Sensitive Association Rule Hiding." In 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.379.

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Garg, Vikram, Anju Singh, and Divakar Singh. "A Survey of Association Rule Hiding Algorithms." In 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.86.

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Femandes, Melissa, and Joanne Gomes. "Heuristic approach for association rule hiding using ECLAT." In 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA). IEEE, 2017. http://dx.doi.org/10.1109/cscita.2017.8066557.

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Doan, Khue, Minh Nguyen Quang, and Bac Le. "Applied Cuckoo Algorithm for Association Rule Hiding Problem." In SoICT 2017: The Eighth International Symposium on Information and Communication Technology. ACM, 2017. http://dx.doi.org/10.1145/3155133.3155150.

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Cheng, Peng. "Identify Risky Rules to Reduce Side Effects in Association Rule Hiding." In CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management. ACM, 2023. http://dx.doi.org/10.1145/3583780.3615259.

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Farea, Afrah, and Ali Karci. "Towards association rule hiding heuristics vs border-based approaches." In 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2015. http://dx.doi.org/10.1109/eleco.2015.7394529.

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Chen, Shan-Tai, Shih-Min Lin, Chi-Yii Tang, and Guei-Yu Lin. "An Improved Algorithm for Completely Hiding Sensitive Association Rule Sets." In 2009 2nd International Conference on Computer Science and its Applications (CSA). IEEE, 2009. http://dx.doi.org/10.1109/csa.2009.5404290.

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Tsai, Yu-Chuan, Shyue-Liang Wang, Cheng-Yu Song, and I.-Hsien Ting. "Privacy and Utility Effects of k-anonymity on Association Rule Hiding." In the The 3rd Multidisciplinary International Social Networks Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2955129.2955169.

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Reports on the topic "ASSOCIATION RULE HIDING"

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Megersa, Kelbesa. Tax Transparency for an Effective Tax System. Institute of Development Studies (IDS), 2021. http://dx.doi.org/10.19088/k4d.2021.070.

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This rapid review examines evidence on the transparency in the tax system and its benefits; e.g. rising revenue, strengthen citizen/state relationship, and rule of law. Improvements in tax transparency can help in strengthening public finances in developing countries that are adversely affected by COVID-19. The current context (i.e. a global pandemic, widespread economic slowdown/recessions, and declining tax revenues) engenders the urgency of improving domestic resource mobilisation (DRM) and the fight against illicit financial flows (IFFs). Even before the advent of COVID-19, developing coun
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