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Статті в журналах з теми "ASSOCIATION RULE HIDING"

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Автор: Grafiati

Опубліковано: 11 вересня 2023

Оновлено: 16 квітня 2024

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1

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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2

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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3

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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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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Анотація:

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

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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6

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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Анотація:

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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7

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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Анотація:

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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8

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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9

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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Анотація:

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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10

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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Анотація:

<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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11

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 (August 1, 2021): 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.

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Анотація:

<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 paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>

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12

Wang, Hui. "Association Rule: From Mining to Hiding." Applied Mechanics and Materials 321-324 (June 2013): 2570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2570.

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Анотація:

Data mining is to discover knowledge which is unknown and hidden in huge database and would be helpful for people understand the data and make decision better. Some knowledge discovered from data mining is considered to be sensitive that the holder of the database will not share because it might cause serious privacy or security problems. Privacy preserving data mining is to hide sensitive knowledge and it is becoming more and more important and attractive. Association rule is one class of the most important knowledge to be mined, so as sensitive association rule hiding. The side-effects of the existing data mining technology are investigated and the representative strategies of association rule hiding are discussed.

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13

Gayathiri, P., and B. Poorna. "Effective Gene Patterned Association Rule Hiding Algorithm for Privacy Preserving Data Mining on Transactional Database." Cybernetics and Information Technologies 17, no. 3 (September 1, 2017): 92–108. http://dx.doi.org/10.1515/cait-2017-0032.

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Анотація:

Abstract Association Rule Hiding methodology is a privacy preserving data mining technique that sanitizes the original database by hide sensitive association rules generated from the transactional database. The side effect of association rules hiding technique is to hide certain rules that are not sensitive, failing to hide certain sensitive rules and generating false rules in the resulted database. This affects the privacy of the data and the utility of data mining results. In this paper, a method called Gene Patterned Association Rule Hiding (GPARH) is proposed for preserving privacy of the data and maintaining the data utility, based on data perturbation technique. Using gene selection operation, privacy linked hidden and exposed data items are mapped to the vector data items, thereby obtaining gene based data item. The performance of proposed GPARH is evaluated in terms of metrics such as number of sensitive rules generated, true positive privacy rate and execution time for selecting the sensitive rules by using Abalone and Taxi Service Trajectory datasets.

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14

B.Jadav, Khyati, Jignesh Vania, and Dhiren R. Patel. "A Survey on Association Rule Hiding Methods." International Journal of Computer Applications 82, no. 13 (November 15, 2013): 20–25. http://dx.doi.org/10.5120/14177-2357.

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15

S, Kasthuri, and Meyyappan T. "Hiding Sensitive Association Rule Using Heuristic Approach." International Journal of Data Mining & Knowledge Management Process 3, no. 1 (January 31, 2013): 57–63. http://dx.doi.org/10.5121/ijdkp.2013.3105.

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16

Murthy, T. Satyanarayana, and N. P. Gopalan. "A Novel Algorithm for Association Rule Hiding." International Journal of Information Engineering and Electronic Business 10, no. 3 (May 8, 2018): 45–50. http://dx.doi.org/10.5815/ijieeb.2018.03.06.

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17

Chaudhari, Chaitrali, and Speril Machado. "Association Rule Hiding for Multi-Relational Database." International Journal of Computer Trends and Technology 30, no. 4 (December 25, 2015): 187–95. http://dx.doi.org/10.14445/22312803/ijctt-v30p133.

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18

Afshari, Mahtab Hossein, Mohammad Naderi Dehkordi, and Mehdi Akbari. "Association rule hiding using cuckoo optimization algorithm." Expert Systems with Applications 64 (December 2016): 340–51. http://dx.doi.org/10.1016/j.eswa.2016.08.005.

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19

Le, Bac, Lien Kieu, and Dat Tran. "DISTORTION-BASED HEURISTIC METHOD FOR SENSITIVE ASSOCIATION RULE HIDING." Journal of Computer Science and Cybernetics 35, no. 4 (October 31, 2019): 337–54. http://dx.doi.org/10.15625/1813-9663/35/4/14131.

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Анотація:

In the past few years, privacy issues in data mining have received considerable attention in the data mining literature. However, the problem of data security cannot simply be solved by restricting data collection or against unauthorized access, it should be dealt with by providing solutions that not only protect sensitive information, but also not affect to the accuracy of the results in data mining and not violate the sensitive knowledge related with individual privacy or competitive advantage in businesses. Sensitive association rule hiding is an important issue in privacy preserving data mining. The aim of association rule hiding is to minimize the side effects on the sanitized database, which means to reduce the number of missing non-sensitive rules and the number of generated ghost rules. Current methods for hiding sensitive rules cause side effects and data loss. In this paper, we introduce a new distortion-based method to hide sensitive rules. This method proposes the determination of critical transactions based on the number of non-sensitive maximal frequent itemsets that contain at least one item to the consequent of the sensitive rule, they can be directly affected by the modified transactions. Using this set, the number of non-sensitive itemsets that need to be considered is reduced dramatically. We compute the smallest number of transactions for modification in advance to minimize the damage to the database. Comparative experimental results on real datasets showed that the proposed method can achieve better results than other methods with fewer side effects and data loss.

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20

Bonam, Janakiramaiah, and Ramamohan Reddy. "Balanced Approach for Hiding Sensitive Association Rules in Data Sharing Environment." International Journal of Information Security and Privacy 8, no. 3 (July 2014): 39–62. http://dx.doi.org/10.4018/ijisp.2014070103.

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Privacy preserving association rule mining protects the sensitive association rules specified by the owner of the data by sanitizing the original database so that the sensitive rules are hidden. In this paper, the authors study a problem of hiding sensitive association rules by carefully modifying the transactions in the database. The algorithm BHPSP calculates the impact factor of items in the sensitive association rules. Then it selects a rule which contains an item with minimum impact factor. The algorithm alters the transactions of the database to hide the sensitive association rule by reducing the loss of other non-sensitive association rules. The quality of a database can be well maintained by greedily selecting the alterations in the database with negligible side effects. The BHPSP algorithm is experimentally compared with a HCSRIL algorithm with respect to the performance measures misses cost and difference between original and sanitized databases. Experimental results are also mentioned demonstrating the effectiveness of the proposed approach.

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21

Garg, Vikram, Anju Singh, and Divakar Singh. "A Hybrid Algorithm for Association Rule Hiding using Representative Rule." International Journal of Computer Applications 97, no. 9 (July 18, 2014): 9–14. http://dx.doi.org/10.5120/17033-7334.

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22

Darwish, Saad M., Magda M. Madbouly, and Mohamed A. El-Hakeem. "A Database Sanitizing Algorithm for Hiding Sensitive Multi-Level Association Rule Mining." International Journal of Computer and Communication Engineering 3, no. 4 (2014): 285–93. http://dx.doi.org/10.7763/ijcce.2014.v3.337.

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23

Wang, Hui. "Hiding Sensitive Association Rules by Adjusting Support." Advanced Materials Research 756-759 (September 2013): 1875–78. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1875.

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Анотація:

Data mining technologies are successfully applied in lots of domains such as business, science research, health care, bioinformatics, financial forecasting and so on and so forth. Knowledge can be discovered by data mining and can help people to make better decisions and benefits. Association rule is one kind of the most popular knowledge discovered by data mining. While at the same time, some association rules extracted from data mining can be considered so sensitive for data holders that they will not like to share and really want to hide. Such kind of side effects of data mining is analyzed by privacy preserving technologies. In this work, we have proposed strategies by adjusting supports and quality measurements of sensitive association rules hiding.

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24

Sharmila, S., and S. Vijayarani. "Heuristic Approach in Association Rule Hiding- A Study." International Journal of Computer Sciences and Engineering 7, no. 5 (May 31, 2019): 300–305. http://dx.doi.org/10.26438/ijcse/v7i5.300305.

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25

Cheng, Peng, Ivan Lee, Chun-Wei Lin, and Jeng-Shyang Pan. "Association rule hiding based on evolutionary multi-objective optimization." Intelligent Data Analysis 20, no. 3 (April 20, 2016): 495–514. http://dx.doi.org/10.3233/ida-160817.

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26

Modak, Masooda, and Rizwana Shaikh. "Privacy Preserving Distributed Association Rule Hiding Using Concept Hierarchy." Procedia Computer Science 79 (2016): 993–1000. http://dx.doi.org/10.1016/j.procs.2016.03.126.

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27

Refaat, Mohamed, H. Aboelseoud, Khalid Shafee, and M. Badr. "Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges." International Journal of Computer Applications 136, no. 6 (February 17, 2016): 11–17. http://dx.doi.org/10.5120/ijca2016908446.

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28

Rajasekaran, M., M. S. Thanabal, and A. Meenakshi. "Association rule hiding using enhanced elephant herding optimization algorithm." Automatika 65, no. 1 (November 29, 2023): 98–107. http://dx.doi.org/10.1080/00051144.2023.2277998.

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29

Mary, A. Geetha, D. P. Acharjya, and N. Ch S. N. Iyengar. "Privacy preservation in fuzzy association rules using rough computing and DSR." Cybernetics and Information Technologies 14, no. 1 (March 1, 2014): 52–71. http://dx.doi.org/10.2478/cait-2014-0005.

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Abstract In the present age of Internet, data is accumulated at a dramatic pace. The accumulated huge data has no relevance, unless it provides certain useful information pertaining to the interest of the organization. But the real challenge lies in hiding sensitive information in order to provide privacy. Therefore, attribute reduction becomes an important aspect for handling such huge database by eliminating superfluous or redundant data to enable a sensitive rule hiding in an efficient manner before it is disclosed to the public. In this paper we propose a privacy preserving model to hide sensitive fuzzy association rules. In our model we use two processes, named a pre-process and post-process to mine fuzzified association rules and to hide sensitive rules. Experimental results demonstrate the viability of the proposed research.

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30

Kharwar, Ankit, Chandni Naik, Niyanta Desai, and Nikita Mistree. "Sensitive Association Rule Hiding using Hybrid Algorithm in Incremental Environment." International Journal of Computer Applications 180, no. 28 (March 20, 2018): 5–9. http://dx.doi.org/10.5120/ijca2018916650.

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31

kamani, Hiren R. "Improved Association Rule Hiding Algorithm for Privacy Preserving Data Mining." IOSR Journal of Engineering 4, no. 7 (July 2014): 36–41. http://dx.doi.org/10.9790/3021-04713641.

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32

Gulwani, Padam. "Association Rule Hiding by Positions Swapping of Support and Confidence." International Journal of Information Technology and Computer Science 4, no. 4 (April 19, 2012): 54–61. http://dx.doi.org/10.5815/ijitcs.2012.04.08.

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33

R. Ponde, Mr Pravin, and Dr S. M. Jagade. "Privacy Preserving by Hiding Association Rule Mining from Transaction Database." IOSR Journal of Computer Engineering 16, no. 5 (2014): 25–31. http://dx.doi.org/10.9790/0661-16522531.

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34

Rao, K. Srinivasa, Venkata Naresh Mandhala, Debnath Bhattacharyya, and Tai-hoon Kim. "An Association Rule hiding Algorithm for Privacy Preserving Data Mining." International Journal of Control and Automation 7, no. 10 (October 31, 2014): 393–404. http://dx.doi.org/10.14257/ijca.2014.7.10.36.

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35

Dehkordi, Mohammad Noderi. "A Novel Association Rule Hiding Approach in OLAP Data Cubes." Indian Journal of Science and Technology 6, no. 2 (February 20, 2013): 1–13. http://dx.doi.org/10.17485/ijst/2013/v6i2.17.

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36

Duraiswamy, K., and N. Maheswari. "Sensitive Items in Privacy Preserving — Association Rule Mining." Journal of Information & Knowledge Management 07, no. 01 (March 2008): 31–35. http://dx.doi.org/10.1142/s0219649208001932.

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Анотація:

Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

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37

Joshi, Apoorva, and Pratima Gautam. "An optimized algorithm for association rule hiding technique using Hybrid Approach." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 832–36. http://dx.doi.org/10.26438/ijcse/v7i1.832836.

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38

R. Ponde, Mr Pravin, Prof Chetan V. Andhare, and Dr S. M. Jagade. "Privacy Preservation by Using AMDSRRC for Hiding Highly Sensitive Association Rule." IOSR Journal of Computer Engineering 16, no. 6 (2014): 60–65. http://dx.doi.org/10.9790/0661-16636065.

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39

Le, Hai Quoc, Somjit Arch-int, Huy Xuan Nguyen, and Ngamnij Arch-int. "Association rule hiding in risk management for retail supply chain collaboration." Computers in Industry 64, no. 7 (September 2013): 776–84. http://dx.doi.org/10.1016/j.compind.2013.04.011.

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40

Verykios, Vassilios S., Emmanuel D. Pontikakis, Yannis Theodoridis, and Liwu Chang. "Efficient algorithms for distortion and blocking techniques in association rule hiding." Distributed and Parallel Databases 22, no. 1 (July 13, 2007): 85–104. http://dx.doi.org/10.1007/s10619-007-7013-0.

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41

Krishnamoorthy, Sathiyapriya, and Kaviya Murugesan. "Protecting the Privacy of Cancer Patients Using Fuzzy Association Rule Hiding." Asian Pacific Journal of Cancer Prevention 20, no. 5 (May 1, 2019): 1437–43. http://dx.doi.org/10.31557/apjcp.2019.20.5.1437.

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42

Telikani, Akbar, and Asadollah Shahbahrami. "Optimizing association rule hiding using combination of border and heuristic approaches." Applied Intelligence 47, no. 2 (April 12, 2017): 544–57. http://dx.doi.org/10.1007/s10489-017-0906-3.

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43

Krishnamoorthy, Sathiyapriya, G. Sudha Sadasivam, M. Rajalakshmi, K. Kowsalyaa, and M. Dhivya. "Privacy Preserving Fuzzy Association Rule Mining in Data Clusters Using Particle Swarm Optimization." International Journal of Intelligent Information Technologies 13, no. 2 (April 2017): 1–20. http://dx.doi.org/10.4018/ijiit.2017040101.

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Анотація:

An association rule is classified as sensitive if its thread of revelation is above certain confidence value. If these sensitive rules were revealed to the public, it is possible to deduce sensitive knowledge from the published data and offers benefit for the business competitors. Earlier studies in privacy preserving association rule mining focus on binary data and has more side effects. But in practical applications the transactions contain the purchased quantities of the items. Hence preserving privacy of quantitative data is essential. The main goal of the proposed system is to hide a group of interesting patterns which contains sensitive knowledge such that modifications have minimum side effects like lost rules, ghost rules, and number of modifications. The proposed system applies Particle Swarm Optimization to a few clusters of particles thus reducing the number of modification. Experimental results demonstrate that the proposed approach is efficient in terms of lost rules, number of modifications, hiding failure with complete avoidance of ghost rules.

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Zhang, Chao, and Linling He. "Data Mining Technology in Teaching Evaluation of Colleges and Universities." SHS Web of Conferences 187 (2024): 04030. http://dx.doi.org/10.1051/shsconf/202418704030.

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Data Mining refers to the large amount of data from the database through algorithmic search reveals implicit, previously unknown and potentially valuable process information[1]. Currently, many areas during the application of data mining. Data mining association rules is one of the most important and most mature technology research methods, association rule mining can find the hidden link between the transaction and meaningful rules. The purpose of this study is to evaluate data mining techniques combined with teaching, to extract useful information from a large number of evaluation data hiding, thereby providing a basis for decision support educational administration department, improve teaching quality.

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V. Waghchaure, Ms Snehal, Mr Nitin J. Khapale, and Ms Badhitala Thulasi. "Minimization of Side Effects of Knowledge Hiding Based On Association Rule Mining." IOSR Journal of Computer Engineering 16, no. 3 (2014): 83–90. http://dx.doi.org/10.9790/0661-16318390.

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Ghiasi, Somayeh, and Mahdi Bateni. "Presenting a Hiding Algorithm for Improving Privacy Preserving in Association Rule Mining." International Journal of Computer Applications 103, no. 10 (October 18, 2014): 31–40. http://dx.doi.org/10.5120/18111-9240.

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47

Afzali, Golnar Assadat, and Shahriar Mohammadi. "Privacy preserving big data mining: association rule hiding using fuzzy logic approach." IET Information Security 12, no. 1 (January 1, 2018): 15–24. http://dx.doi.org/10.1049/iet-ifs.2015.0545.

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48

Ndruru, Eferoni, and Taronisokhi Zebua. "Application of Text Message Held in Image Using Combination of Least Significant Bit Method and One Time Pad." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 13, no. 4 (October 31, 2019): 323. http://dx.doi.org/10.22146/ijccs.46401.

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Stenography and security are one of the techniques to develop art in securing data. Stenography has the most important aspect is the level of security in data hiding, which makes the third party unable to detect some information that has been secured. Usually used to hide textinformationThe (LSB) algorithm is one of the basic algorithms proposed by Arawak and Giant in 1994 to determine the frequent item set for Boolean association rules. A priory algorithm includes the type of association rules in data mining. The rule that states associations between attributes are often called affinity analysis or market basket analysis. OTP can be widely used in business. With the knowledge of text message, concealment techniques will make it easier for companies to know the number of frequencies of sales data, making it easier for companies to take an appropriate transaction action. The results of this study, hide the text message on the image (image) by using a combination of LSB and Otp methods.

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Gopagoni, Praveen Kumar, and Mohan Rao S K. "Distributed elephant herding optimization for grid-based privacy association rule mining." Data Technologies and Applications 54, no. 3 (May 15, 2020): 365–82. http://dx.doi.org/10.1108/dta-07-2019-0104.

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

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Praveena, Kurapati, Gudla Sirisha, Satukumati Babu, and Panchala Rao. "Efficient Method in Association Rule Hiding for Privacy Preserving with Data Mining Approach." Ingénierie des systèmes d information 24, no. 1 (April 20, 2019): 47–50. http://dx.doi.org/10.18280/isi.240106.

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