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Artykuły w czasopismach na temat „RULE HIDING”

Autor: Grafiati

Data publikacji: 11 września 2023

Data aktualizacji: 16 kwietnia 2024

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1

Verykios, V. S., A. K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni. "Association rule hiding." IEEE Transactions on Knowledge and Data Engineering 16, no. 4 (2004): 434–47. http://dx.doi.org/10.1109/tkde.2004.1269668.

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Khurana, Garvit. "Association Rule Hiding using Hash Tree." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 787–89. http://dx.doi.org/10.31142/ijtsrd23037.

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3

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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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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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

Mohan, S. Vijayarani, and Tamilarasi Angamuthu. "Association Rule Hiding in Privacy Preserving Data Mining." International Journal of Information Security and Privacy 12, no. 3 (2018): 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.

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This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing 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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6

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

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7

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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8

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 (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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9

Wang, Shyue-Liang, Bhavesh Parikh, and Ayat Jafari. "Hiding informative association rule sets." Expert Systems with Applications 33, no. 2 (2007): 316–23. http://dx.doi.org/10.1016/j.eswa.2006.05.022.

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10

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

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<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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11

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 (2017): 92–108. http://dx.doi.org/10.1515/cait-2017-0032.

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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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12

B., Suma, and Shobha G. "Association rule hiding using integer linear programming." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (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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13

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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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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14

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 (2019): 337–54. http://dx.doi.org/10.15625/1813-9663/35/4/14131.

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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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15

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 (2014): 9–14. http://dx.doi.org/10.5120/17033-7334.

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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 (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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17

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

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18

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

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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 (2018): 45–50. http://dx.doi.org/10.5815/ijieeb.2018.03.06.

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

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

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Wette, Philip, and Holger Karl. "Which flows are hiding behind my wildcard rule?" ACM SIGCOMM Computer Communication Review 43, no. 4 (2013): 541–42. http://dx.doi.org/10.1145/2534169.2491710.

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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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SCHWINGHAMMER, JAN, LARS BIRKEDAL, FRANÇOIS POTTIER, BERNHARD REUS, KRISTIAN STØVRING, and HONGSEOK YANG. "A step-indexed Kripke model of hidden state." Mathematical Structures in Computer Science 23, no. 1 (2012): 1–54. http://dx.doi.org/10.1017/s0960129512000035.

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Frame and anti-frame rules have been proposed as proof rules for modular reasoning about programs. Frame rules allow the hiding of irrelevant parts of the state during verification, whereas the anti-frame rule allows the hiding of local state from the context.We discuss the semantic foundations of frame and anti-frame rules, and present the first sound model for Charguéraud and Pottier's type and capability system including both of these rules. The model is a possible worlds model based on the operational semantics and step-indexed heap relations, and the worlds are given by a recursively defined metric space. We also extend the model to account for Pottier's generalised frame and anti-frame rules, where invariants are generalised to families of invariants indexed over preorders. This generalisation enables reasoning about some well-bracketed as well as (locally) monotone uses of local state.

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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 (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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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 (2016): 495–514. http://dx.doi.org/10.3233/ida-160817.

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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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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 (2016): 11–17. http://dx.doi.org/10.5120/ijca2016908446.

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

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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 (2018): 5–9. http://dx.doi.org/10.5120/ijca2018916650.

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

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

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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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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 (2014): 393–404. http://dx.doi.org/10.14257/ijca.2014.7.10.36.

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

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Cheng, Peng, John F. Roddick, Shu-Chuan Chu, and Chun-Wei Lin. "Privacy preservation through a greedy, distortion-based rule-hiding method." Applied Intelligence 44, no. 2 (2015): 295–306. http://dx.doi.org/10.1007/s10489-015-0671-0.

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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 (2017): 1–20. http://dx.doi.org/10.4018/ijiit.2017040101.

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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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41

Qi, Gaoxin, Jichao Li, Xueming Xu, Gang Chen, and Kewei Yang. "An attack–defense game model in infrastructure networks under link hiding." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 11 (2022): 113109. http://dx.doi.org/10.1063/5.0112907.

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Our increasing dependence on infrastructure networks leads to growing concerns over the protection of these networks. Many methods have been proposed to select protective strategies by combining complex network theory and game theory. However, the misleading effect of hidden links is not considered in previous methods. This work creates an information gap between attackers and defenders by partly hiding network links to mislead the attacker in the game. We first introduce the rule of link hiding that depends on the nodes’ property, where the number of hidden links has a maximum value. Additionally, based on the Stackelberg game model, we establish an attack–defense game model with link hiding strategies considering node property and cost constraints. Finally, we conduct experiments in a scale-free network and an existing power grid. The experimental results show that the defender tends to combine first-mover advantage and link hiding to get a better payoff under more different costs of the nodes. Hiding half of the links in the existing power grid can effectively reduce network damage by about [Formula: see text]% on average, with the two sides investing the same resources. The effect of link hiding could be more obvious when the attacker owns more resources than the defender. When an attacker employs the high-degree attacking strategy, the proposed link hiding method can help the defender reduce the damage to the network by 12.2% compared to the link reconnecting method.

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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 (2019): 832–36. http://dx.doi.org/10.26438/ijcse/v7i1.832836.

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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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Zamani Boroujeni, Farsad, and Doryaneh Hossein Afshari. "An Efficient Rule-Hiding Method for Privacy Preserving in Transactional Databases." Journal of Computing and Information Technology 25, no. 4 (2018): 279–90. http://dx.doi.org/10.20532/cit.2017.1003680.

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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 (2013): 776–84. http://dx.doi.org/10.1016/j.compind.2013.04.011.

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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 (2007): 85–104. http://dx.doi.org/10.1007/s10619-007-7013-0.

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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 (2019): 1437–43. http://dx.doi.org/10.31557/apjcp.2019.20.5.1437.

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

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Jalid, Alfian Abdul, Agus Harjoko, and Anny Kartika Sari. "Steganographic Model for encrypted messages based on DNA Encoding." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 1 (2021): 43. http://dx.doi.org/10.22146/ijccs.61767.

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Streszczenie:

Information has become an inseparable part of human life. Some information that is considered important, such as state or company documents, require more security to ensure its confidentiality. One way of securing information is by hiding the information in certain media using steganography techniques. Steganography is a method of hiding information into other files to make it invisible. One of the most frequently used steganographic methods is Least Significant Bit (LSB).In this study, the LSB method will be modified using DNA Encoding and Chargaff's Rule. Chargaff's Rule or complementary base pairing rule is used to construct a complementary strand. The modification of the LSB method using DNA encoding and Chargaff's Rule is expected to increase the security of the information.The MSE test results show the average value of the LSB method is 0.000236368, while the average value for the DNA Encoding-based Steganography method is 0.000770917. The average PSNR value for the LSB method was 76.82 dB while the DNA Encoding-based Steganography method had an average value of 70.88 dB. The time of inserting and extracting messages using the Steganography method based on DNA Encoding is relatively longer than the LSB method because of its higher algorithmic complexity. The message security of the DNA Encoding-based Steganography method is better because there is encryption in the algorithm compared to the LSB method which does not have encryption.

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

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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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