Literatura académica sobre el tema "ASSOCIATION RULE HIDING"
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Artículos de revistas sobre el tema "ASSOCIATION RULE HIDING"
Khurana, Garvit. "Association Rule Hiding using Hash Tree". International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (30 de abril de 2019): 787–89. http://dx.doi.org/10.31142/ijtsrd23037.
Texto completoVerykios, V. S., A. K. Elmagarmid, E. Bertino, Y. Saygin y E. Dasseni. "Association rule hiding". IEEE Transactions on Knowledge and Data Engineering 16, n.º 4 (abril de 2004): 434–47. http://dx.doi.org/10.1109/tkde.2004.1269668.
Texto completoVerykios, Vassilios S. "Association rule hiding methods". Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, n.º 1 (enero de 2013): 28–36. http://dx.doi.org/10.1002/widm.1082.
Texto completoWang, Hui. "Hiding Sensitive Association Rules by Sanitizing". Advanced Materials Research 694-697 (mayo de 2013): 2317–21. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2317.
Texto completoQuoc Le, Hai, Somjit Arch-int y 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.
Texto completoWang, Hui. "Strategies for Sensitive Association Rule Hiding". Applied Mechanics and Materials 336-338 (julio de 2013): 2203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2203.
Texto completoMohan, S. Vijayarani y Tamilarasi Angamuthu. "Association Rule Hiding in Privacy Preserving Data Mining". International Journal of Information Security and Privacy 12, n.º 3 (julio de 2018): 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.
Texto completoWang, Shyue-Liang, Bhavesh Parikh y Ayat Jafari. "Hiding informative association rule sets". Expert Systems with Applications 33, n.º 2 (agosto de 2007): 316–23. http://dx.doi.org/10.1016/j.eswa.2006.05.022.
Texto completoÖztürk, Ahmet Cumhur y Belgin Ergenç. "Dynamic Itemset Hiding Algorithm for Multiple Sensitive Support Thresholds". International Journal of Data Warehousing and Mining 14, n.º 2 (abril de 2018): 37–59. http://dx.doi.org/10.4018/ijdwm.2018040103.
Texto completoB., Suma y Shobha G. "Association rule hiding using integer linear programming". International Journal of Electrical and Computer Engineering (IJECE) 11, n.º 4 (1 de agosto de 2021): 3451. http://dx.doi.org/10.11591/ijece.v11i4.pp3451-3458.
Texto completoTesis sobre el tema "ASSOCIATION RULE HIDING"
LaMacchia, Carolyn. "Improving the Scalability of an Exact Approach for Frequent Item Set Hiding". NSUWorks, 2013. http://nsuworks.nova.edu/gscis_etd/205.
Texto completoVARSHNEY, PEEYUSH. "CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING". Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16143.
Texto completoVARSHNEY, PEEYUSH. "CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING". Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16318.
Texto completoSaikia, Bikramjit y Debkumar Bhowmik. "Study of Association Rule Mining and Different Hiding Techniques". Thesis, 2009. http://ethesis.nitrkl.ac.in/991/1/Thesis.pdf.
Texto completoChan, Ching-yi y 詹景逸. "A Study for Association Rule Hiding Using the Evaluation of Side-Effec". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/44956271738886770834.
Texto completo國立臺南大學
資訊教育研究所碩士班
93
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 requirement of this method are discussed. We also present an experiment showing an example of this methodology.
Kao, Tai-wei y 高黛威. "Hiding dynamic sensitive association rules in incremental data". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/29229249691498416855.
Texto completo國立臺灣科技大學
資訊工程系
101
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 policy. These both are the challenges for protecting sensitive association rules. Most exist technologies of hiding sensitive association rules cannot handle dynamic data and sensitive rules effectively. For solving these problems, this paper proposed a framework to protect dynamic sensitive association rules in incremental environment, HSAi and HDSA. HSAi is the algorithm to protect sensitive association rule in incremental data and we design the strategy to select appropriate victim transactions and items to delete them in order to hide sensitive association rules. HDSA is the algorithm for protecting dynamic sensitive rules, including adding and deleting. The mean of the deleting sensitive rule is the association rule that hidden can show again in the mining result. The goals of HSAi and HDSA are not only protecting sensitive rules but also producing least side effect from released dataset. Experiment results represent that the framework situation of incremental data and dynamic sensitive rules both can cause least side effects and maintain a desirable quality of sanitized database as well.
Lai, Ting-Zheng y 賴廷政. "A Study of Hiding Collaborative Recommendation Association Rules on Horizontally Partitioned Data". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/19596225694306918568.
Texto completo義守大學
資訊管理學系碩士班
98
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. However, in the real world, data may exist in distributed environment and owned by non-trusting parties that might be willing to collaborate. In this work, we propose a framework to hide collaborative recommendation association rules where the data sets are horizontally partitioned and owned by non-trusting parties. Algorithms to hide the collaborative recommendation association rules and to merge the sanitized data sets are introduced. Performance and various side effects of the proposed approach are analyzed numerically. Comparisons with trusting-third-party approach are reported. The proposed non-trusting-third-party approach shows better processing time, with similar side effects.
Libros sobre el tema "ASSOCIATION RULE HIDING"
Gkoulalas-Divanis, Aris y Vassilios S. Verykios. Association Rule Hiding for Data Mining. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1.
Texto completoGkoulalas-Divanis, Aris. Association rule hiding for data mining. New York: Springer, 2010.
Buscar texto completoGkoulalas-Divanis, Aris y Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer, 2012.
Buscar texto completoCapítulos de libros sobre el tema "ASSOCIATION RULE HIDING"
Gkoulalas-Divanis, Aris y Vassilios S. Verykios. "Classes of Association Rule Hiding Methodologies". En Advances in Database Systems, 17–20. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1_3.
Texto completoSharmila, S. y S. Vijayarani. "Association Rule Hiding Using Firefly Optimization Algorithm". En Advances in Intelligent Systems and Computing, 699–708. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16660-1_68.
Texto completoGopalan, N. P. y T. Satyanarayana Murthy. "Association Rule Hiding Using Chemical Reaction Optimization". En Advances in Intelligent Systems and Computing, 249–55. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_19.
Texto completoMogtaba, Shyma y Eiman Kambal. "Association Rule Hiding for Privacy Preserving Data Mining". En Advances in Data Mining. Applications and Theoretical Aspects, 320–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41561-1_24.
Texto completoVerykios, Vassilios S. y Aris Gkoulalas-Divanis. "A Survey of Association Rule Hiding Methods for Privacy". En Privacy-Preserving Data Mining, 267–89. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5_11.
Texto completoJagtap, Nitin y Krishankant P. Adhiya. "Data sanitisation techniques for transactional datasets using association rule hiding techniques". En Recent Advances in Material, Manufacturing, and Machine Learning, 1168–76. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003370628-47.
Texto completoYou, Na Young, Kwang Sun Ryu, Jae Ho Kim, Ha Ye Jin Kang, Sang Won Lee, Kui Son Choi y Hyo Soung Cha. "Association Rule Mining Method to Predict Coronary Artery Disease: KNHANES 2016–2018". En Advances in Intelligent Information Hiding and Multimedia Signal Processing, 274–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_34.
Texto completoZheng, Huilin, Hyun Woo Park y Keun Ho Ryu. "An Efficient Association Rule Mining Method to Predict Diabetes Mellitus: KNHANES 2013–2015". En Advances in Intelligent Information Hiding and Multimedia Signal Processing, 241–49. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9714-1_26.
Texto completoZhu, Jianming y Zhanyu Li. "Privacy Preserving Association Rule Mining Algorithm Based on Hybrid Partial Hiding Strategy". En LISS 2013, 1065–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40660-7_160.
Texto completoAudichya, Dinesh, Prashant Sharma y Pankaj Kumar Vaishnav. "Determination of Avalanche Effect to Compute the Efficiency of Association Rule Hiding Algorithms". En Artificial Intelligence and Sustainable Computing, 749–59. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1653-3_55.
Texto completoActas de conferencias sobre el tema "ASSOCIATION RULE HIDING"
Zhu, Zutao y Wenliang Du. "K-anonymous association rule hiding". En the 5th ACM Symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1755688.1755726.
Texto completoFovino, Igor Nai y Alberto Trombetta. "Information driven association rule hiding algorithms". En 2008 1st International Conference on Information Technology (IT 2008). IEEE, 2008. http://dx.doi.org/10.1109/inftech.2008.4621664.
Texto completoZhang, Xiaoming y Xi Qiao. "New Approach for Sensitive Association Rule Hiding". En 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.379.
Texto completoGarg, Vikram, Anju Singh y Divakar Singh. "A Survey of Association Rule Hiding Algorithms". En 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.86.
Texto completoFemandes, Melissa y Joanne Gomes. "Heuristic approach for association rule hiding using ECLAT". En 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA). IEEE, 2017. http://dx.doi.org/10.1109/cscita.2017.8066557.
Texto completoDoan, Khue, Minh Nguyen Quang y Bac Le. "Applied Cuckoo Algorithm for Association Rule Hiding Problem". En SoICT 2017: The Eighth International Symposium on Information and Communication Technology. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3155133.3155150.
Texto completoCheng, Peng. "Identify Risky Rules to Reduce Side Effects in Association Rule Hiding". En CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583780.3615259.
Texto completoFarea, Afrah y Ali Karci. "Towards association rule hiding heuristics vs border-based approaches". En 2015 9th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2015. http://dx.doi.org/10.1109/eleco.2015.7394529.
Texto completoChen, Shan-Tai, Shih-Min Lin, Chi-Yii Tang y Guei-Yu Lin. "An Improved Algorithm for Completely Hiding Sensitive Association Rule Sets". En 2009 2nd International Conference on Computer Science and its Applications (CSA). IEEE, 2009. http://dx.doi.org/10.1109/csa.2009.5404290.
Texto completoTsai, Yu-Chuan, Shyue-Liang Wang, Cheng-Yu Song y I.-Hsien Ting. "Privacy and Utility Effects of k-anonymity on Association Rule Hiding". En the The 3rd Multidisciplinary International Social Networks Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2955129.2955169.
Texto completoInformes sobre el tema "ASSOCIATION RULE HIDING"
Megersa, Kelbesa. Tax Transparency for an Effective Tax System. Institute of Development Studies (IDS), enero de 2021. http://dx.doi.org/10.19088/k4d.2021.070.
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