Littérature scientifique sur le sujet « RULE HIDING »
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Articles de revues sur le sujet "RULE HIDING"
Verykios, V. S., A. K. Elmagarmid, E. Bertino, Y. Saygin et E. Dasseni. « Association rule hiding ». IEEE Transactions on Knowledge and Data Engineering 16, no 4 (avril 2004) : 434–47. http://dx.doi.org/10.1109/tkde.2004.1269668.
Texte intégralKhurana, Garvit. « Association Rule Hiding using Hash Tree ». International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (30 avril 2019) : 787–89. http://dx.doi.org/10.31142/ijtsrd23037.
Texte intégralQuoc Le, Hai, Somjit Arch-int et 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.
Texte intégralWang, Hui. « Hiding Sensitive Association Rules by Sanitizing ». Advanced Materials Research 694-697 (mai 2013) : 2317–21. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2317.
Texte intégralMohan, S. Vijayarani, et Tamilarasi Angamuthu. « Association Rule Hiding in Privacy Preserving Data Mining ». International Journal of Information Security and Privacy 12, no 3 (juillet 2018) : 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.
Texte intégralVerykios, Vassilios S. « Association rule hiding methods ». Wiley Interdisciplinary Reviews : Data Mining and Knowledge Discovery 3, no 1 (janvier 2013) : 28–36. http://dx.doi.org/10.1002/widm.1082.
Texte intégralWang, Hui. « Strategies for Sensitive Association Rule Hiding ». Applied Mechanics and Materials 336-338 (juillet 2013) : 2203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2203.
Texte intégralÖztürk, Ahmet Cumhur, et Belgin Ergenç. « Dynamic Itemset Hiding Algorithm for Multiple Sensitive Support Thresholds ». International Journal of Data Warehousing and Mining 14, no 2 (avril 2018) : 37–59. http://dx.doi.org/10.4018/ijdwm.2018040103.
Texte intégralWang, Shyue-Liang, Bhavesh Parikh et Ayat Jafari. « Hiding informative association rule sets ». Expert Systems with Applications 33, no 2 (août 2007) : 316–23. http://dx.doi.org/10.1016/j.eswa.2006.05.022.
Texte intégralB., Suma, et Shobha G. « Privacy preserving association rule hiding using border based approach ». Indonesian Journal of Electrical Engineering and Computer Science 23, no 2 (1 août 2021) : 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.
Texte intégralThèses sur le sujet "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.
Texte intégralVARSHNEY, PEEYUSH. « CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING ». Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16143.
Texte intégralVARSHNEY, PEEYUSH. « CLOUD FRAMEWORK FOR ASSOCIATION RULE HIDING ». Thesis, 2017. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16318.
Texte intégralSaikia, Bikramjit, et Debkumar Bhowmik. « Study of Association Rule Mining and Different Hiding Techniques ». Thesis, 2009. http://ethesis.nitrkl.ac.in/991/1/Thesis.pdf.
Texte intégralChiang, Chia Ming, et 江家明. « A New Approach for Sensitive Rule Hiding by Considering Side Effects ». Thesis, 2003. http://ndltd.ncl.edu.tw/handle/96048877295787722429.
Texte intégral國立清華大學
資訊工程學系
91
As the growth of computer technology has been advanced, the amount of data has been increasing with an extremely fast rate. A variety of methods for knowledge discovery and data mining have been developed to help people digest the huge number of data. One of the popular data mining research issues is association rule mining. Based on the techniques for mining association rules, the correlations between data items can be identified. However, the misuses of these methods may bring undesired side effects to the people. Recently, researchers have made great efforts at hiding association rules. In this thesis, we develop a new approach that can hide the sensitive information without generating undesired side effects. Our approach consists of three steps corresponding to three possible problems. At first, we adopt the template concept to identify either the set of modifiable transactions or the set of probably affected association rules. For efficiency, we design indexing facilities for fast retrieval of the required information in the transaction database. Second, among the selected transactions for hiding sensitive rules, we further select the transactions that will not hide any of the non-sensitive rules. At the third step, we examine these selected transactions to avoid generating extra rules. Iteratively, sensitive rules can be hidden and the undesired side effects are avoided. In the experiments, we show the effectiveness of our approach according to the three conditions and analyze the performance of different methods for database modifications. Moreover, the results also show that our proposed approach has perfect scalability to the database size. Specifically, the time of the approach that considers all the three conditions is just a little bit slower than the time of the one that do not consider the two side effects.
Chan, Ching-yi, et 詹景逸. « A Study for Association Rule Hiding Using the Evaluation of Side-Effec ». Thesis, 2005. http://ndltd.ncl.edu.tw/handle/44956271738886770834.
Texte intégral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.
Yang, Jei-Hung, et 楊介宏. « Hiding Sensitive Rules Based on Transaction Grouping ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58538882177123969090.
Texte intégral中原大學
資訊工程研究所
98
As the prevalent development of network technology, information sharing and communication is frequent in daily life. Although data mining techniques help people find the important rules among data, people also have to take the risk of sensitive information disclosed. In addition to hiding sensitive rules, recent researches also start discussing the reduction or avoidance of unexpected side effects, including the hiding of non-sensitive rules (lost rules) and the creation of non-existent rules (false rules). This thesis aims at a small amount of transaction modifications and proposes a method of rule hiding. The method can recover lost rules and keep sensitive rules hidden. Besides, we propose an efficient index structure for a quick retrieval of transactions during the hiding process. The experiments verify that our method can hide all sensitive rules and recover at most 40% lost rules. The index structure reduces about 85% retrieval time on average.
Kao, Tai-wei, et 高黛威. « Hiding dynamic sensitive association rules in incremental data ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/29229249691498416855.
Texte intégral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, et 賴廷政. « A Study of Hiding Collaborative Recommendation Association Rules on Horizontally Partitioned Data ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/19596225694306918568.
Texte intégral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.
Livres sur le sujet "RULE HIDING"
Gkoulalas-Divanis, Aris, et 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.
Texte intégralGkoulalas-Divanis, Aris. Association rule hiding for data mining. New York : Springer, 2010.
Trouver le texte intégralGkoulalas-Divanis, Aris, et Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer, 2012.
Trouver le texte intégralWallace, Jeremy L. Seeking Truth and Hiding Facts. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197627655.001.0001.
Texte intégralChapitres de livres sur le sujet "RULE HIDING"
Gkoulalas-Divanis, Aris, et Vassilios S. Verykios. « Classes of Association Rule Hiding Methodologies ». Dans Advances in Database Systems, 17–20. Boston, MA : Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1_3.
Texte intégralSharmila, S., et S. Vijayarani. « Association Rule Hiding Using Firefly Optimization Algorithm ». Dans 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.
Texte intégralGopalan, N. P., et T. Satyanarayana Murthy. « Association Rule Hiding Using Chemical Reaction Optimization ». Dans Advances in Intelligent Systems and Computing, 249–55. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1592-3_19.
Texte intégralMogtaba, Shyma, et Eiman Kambal. « Association Rule Hiding for Privacy Preserving Data Mining ». Dans 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.
Texte intégralNatwichai, Juggapong, Maria E. Orlowska et Xingzhi Sun. « Hiding Sensitive Associative Classification Rule by Data Reduction ». Dans Advanced Data Mining and Applications, 310–22. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_29.
Texte intégralKatsarou, Aliki, Gkoulalas-Divanis Aris et Vassilios S. Verykios. « Reconstruction-based Classification Rule Hiding through Controlled Data Modification ». Dans IFIP Advances in Information and Communication Technology, 449–58. Boston, MA : Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0221-4_53.
Texte intégralVerykios, Vassilios S., et Aris Gkoulalas-Divanis. « A Survey of Association Rule Hiding Methods for Privacy ». Dans Privacy-Preserving Data Mining, 267–89. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5_11.
Texte intégralYampolskiy, Roman V., Jovan D. Rebolledo-Mendez et Musa M. Hindi. « Password Protected Visual Cryptography via Cellular Automaton Rule 30 ». Dans Transactions on Data Hiding and Multimedia Security IX, 57–67. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55046-1_4.
Texte intégralNatwichai, Juggapong, Xingzhi Sun et Xue Li. « A Heuristic Data Reduction Approach for Associative Classification Rule Hiding ». Dans PRICAI 2008 : Trends in Artificial Intelligence, 140–51. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89197-0_16.
Texte intégralCheng, Peng, Shu-Chuan Chu, Chun-Wei Lin et John F. Roddick. « Distortion-Based Heuristic Sensitive Rule Hiding Method – The Greedy Way ». Dans Modern Advances in Applied Intelligence, 77–86. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07455-9_9.
Texte intégralActes de conférences sur le sujet "RULE HIDING"
Zhu, Zutao, et Wenliang Du. « K-anonymous association rule hiding ». Dans the 5th ACM Symposium. New York, New York, USA : ACM Press, 2010. http://dx.doi.org/10.1145/1755688.1755726.
Texte intégralFovino, Igor Nai, et Alberto Trombetta. « Information driven association rule hiding algorithms ». Dans 2008 1st International Conference on Information Technology (IT 2008). IEEE, 2008. http://dx.doi.org/10.1109/inftech.2008.4621664.
Texte intégralJinturkar, R. S., et S. Kolkur. « Measuring side effects of rule hiding ». Dans the International Conference & Workshop. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980128.
Texte intégralJinturkar, S. Rahul, et Seema Kolkur. « Measuring side effects of rule hiding ». Dans the International Conference & Workshop. New York, New York, USA : ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980382.
Texte intégralZhang, Xiaoming, et Xi Qiao. « New Approach for Sensitive Association Rule Hiding ». Dans 2008 International Workshop on Geoscience and Remote Sensing (ETT and GRS). IEEE, 2008. http://dx.doi.org/10.1109/ettandgrs.2008.379.
Texte intégralGarg, Vikram, Anju Singh et Divakar Singh. « A Survey of Association Rule Hiding Algorithms ». Dans 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.86.
Texte intégralCheng, Peng. « Identify Risky Rules to Reduce Side Effects in Association Rule Hiding ». Dans 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.
Texte intégralFemandes, Melissa, et Joanne Gomes. « Heuristic approach for association rule hiding using ECLAT ». Dans 2017 2nd International Conference on Communication Systems, Computing and IT Applications (CSCITA). IEEE, 2017. http://dx.doi.org/10.1109/cscita.2017.8066557.
Texte intégralDoan, Khue, Minh Nguyen Quang et Bac Le. « Applied Cuckoo Algorithm for Association Rule Hiding Problem ». Dans 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.
Texte intégralWette, Philip, et Holger Karl. « Which flows are hiding behind my wildcard rule ? » Dans SIGCOMM'13 : ACM SIGCOMM 2013 Conference. New York, NY, USA : ACM, 2013. http://dx.doi.org/10.1145/2486001.2491710.
Texte intégralRapports d'organisations sur le sujet "RULE HIDING"
Megersa, Kelbesa. Tax Transparency for an Effective Tax System. Institute of Development Studies (IDS), janvier 2021. http://dx.doi.org/10.19088/k4d.2021.070.
Texte intégral