Relevant bibliographies by topics / ASSOCIATION RULE HIDING

Academic literature on the topic 'ASSOCIATION RULE HIDING'

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Published: 11 September 2023
Last updated: 16 April 2024

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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 (April 30, 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 (April 2004): 434–47. http://dx.doi.org/10.1109/tkde.2004.1269668.

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

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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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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 rule hiding problem. We then formulate two heuristics in order to (a) specify the victim items based on the characteristics of the intersection lattice of frequent itemsets and (b) identify transactions for data sanitization based on the weight of transactions. Next, we propose a new algorithm for hiding a specific set of sensitive association rules with minimum side effects and low complexity. Finally, experiments were carried out to clarify the efficiency of the proposed approach. Our results showed that the proposed algorithm, AARHIL, achieved minimum side effects and CPU-Time when compared to current similar state of the art approaches in the context of hiding a specified set of sensitive association rules.
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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 support of the left hand and decreasing the support of the right hand. Quality measurements for sensitive association rules hiding are presented.
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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 (July 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 information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.
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Wang, Shyue-Liang, Bhavesh Parikh, and Ayat Jafari. "Hiding informative association rule sets." Expert Systems with Applications 33, no. 2 (August 2007): 316–23. http://dx.doi.org/10.1016/j.eswa.2006.05.022.

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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 (April 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 solutions consider single support thresholds and assume that the databases are static, which is not true in real life. In this article, the authors propose a novel itemset hiding algorithm designed for the dynamic database environment and consider multiple itemset support thresholds. Performance comparisons of the algorithm is done with two dynamic algorithms on six different databases. Findings show that their dynamic algorithm is more efficient in terms of execution time and information loss and guarantees to hide all sensitive itemsets.
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B., Suma, and Shobha G. "Association rule hiding using integer linear programming." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (August 1, 2021): 3451. http://dx.doi.org/10.11591/ijece.v11i4.pp3451-3458.

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<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>
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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 exact approach that generates an extension to the original database that, when combined with the original database, limits the discovery of sensitive association rules without impacting other non-sensitive information. To generate the database extension, this method formulates a constraint optimization problem (COP). Solving the COP formulation is the dominant factor in the computational resource requirements of the exact approach. This dissertation developed heuristics that address the scalability of the exact hiding method. The heuristics are directed at improving the performance of COP solver by reducing the size of the COP formulation without significantly affecting the quality of the solutions generated. The first heuristic decomposes the COP formulation into multiple smaller problem instances that are processed separately by the COP solver to generate partial extensions of the database. The smaller database extensions are then combined to form a database extension that is close to the database extension generated with the original, larger COP formulation. The second heuristic evaluates the revised border used to formulate the COP and reduces the number of variables and constraints by selectively substituting multiple item sets with composite variables. Solving the COP with fewer variables and constraints reduces the computational cost of the processing. Results of heuristic processing were compared with an existing exact approach based on the size of the database extension, the ability to hide sensitive data, and the impact on nonsensitive data.
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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, implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. However, a fundamental issue, and still not adequately examined, is demand to balance the privacy of the revealed data with the legitimate needs of the data users. The rule is characterizing as sensitive if its disclosure threat is above a certain privacy threshold. Sometimes, sensitive rules should not disclose to the public, since among other things, they may use for inferring sensitive data, or they may offer enterprise competitors with an advantage. Therefore, next I worked with some association rule hiding algorithms and examined their performances to analyse their time complexity orderly and the affect that they have in the original database.
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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, implementation and algorithmic features mainly focusing on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. However, a key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public, since among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. Therefore, next I worked with some association rule hiding algorithms and examined their performances in order to analyse their time complexity and the impact that they have in the original database.
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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 on those that also arise in frequent item set mining. This algorithm has given us new capabilities to identify associations in large data sets. But a key problem, and still not sufficiently investigated, is the need to balance the confidentiality of the disclosed data with the legitimate needs of the data users. One rule is characterized as sensitive if its disclosure risk is above a certain privacy threshold. Sometimes, sensitive rules should not be disclosed to the public, since among other things, they may be used for inferring sensitive data, or they may provide business competitors with an advantage. So, next we worked with some association rule hiding algorithms and examined their performances in order to analyze their time complexity and the impact that they have in the original database. We worked on two different side effects – one was the number of new rules generated during the hiding process and the other one was the number of non-sensitive rules lost during the process.
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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
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.
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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
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.
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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
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.
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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. Boston, MA: 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. New York: 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, 17–20. Boston, MA: 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, 699–708. Cham: 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, 249–55. Singapore: 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, 320–33. Cham: 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, 267–89. Boston, MA: 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, 1168–76. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003370628-47.

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You, Na Young, Kwang Sun Ryu, Jae Ho Kim, Ha Ye Jin Kang, Sang Won Lee, Kui Son Choi, and Hyo Soung Cha. "Association Rule Mining Method to Predict Coronary Artery Disease: KNHANES 2016–2018." In 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.

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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, 241–49. Singapore: 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, 1065–70. Berlin, Heidelberg: 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, 749–59. Singapore: 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. New York, New York, USA: 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. New York, NY, USA: 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. New York, NY, USA: 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. New York, New York, USA: 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), January 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 countries’ tax systems were facing several challenges, including weak tax administrations, low taxpayer morale and “hard-to-tax” sectors. The presence of informational asymmetry (i.e. low tax transparency) between taxpayers and tax authorities generates loopholes for abuse of the tax system. It allows the hiding of wealth abroad with a limited risk of being caught. Cases of such behaviour that are exposed without proper penalty may result in a decline in the morale of citizens and a lower level of voluntary compliance with tax legislation. A number of high-profile tax leaks and scandals have undermined public confidence in the fairness of tax systems and generated a strong demand for effective counteraction and tax transparency. One of the key contributing factors to lower tax revenues in developing countries (that is linked to low tax transparency) is a high level of IFFs. These flows, including international tax evasion and the laundering of corruption proceeds, build a major obstacle to successful DRM efforts. Research has also identified an association between organisational transparency (e.g. transparency by businesses and tax authorities) and stakeholder trust (e.g. between citizens and the state). However, the evidence is mixed as to how transparency in particular influences trust and perceptions of trustworthiness.
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