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Dehkordi, Mohammad Naderi
- A Novel Association Rule Hiding Approach in OLAP Data Cubes
Abstract Views :532 |
PDF Views:174
Authors
Affiliations
1 Computer Engineering Department,Najafabad branch, Islamic Azad University, Esfahan, IR
1 Computer Engineering Department,Najafabad branch, Islamic Azad University, Esfahan, IR
Source
Indian Journal of Science and Technology, Vol 6, No 2 (2013), Pagination: 4063-4075Abstract
Data mining services require exact input data for their outcomes to be significant, but privacy concerns may influence users to provide fake information. We study here, with respect to mining association rules, whether or not users can be confident to provide correct information by ensuring that the mining process cannot, with any reasonable degree of certainty, breach their privacy. A data warehouse stores current and historical records consolidated from multiple transactional systems. Protecting data warehouses is of rising interest, particularly in view of areas where data are sold in pieces to third parties for data mining studies. In this case, current normal data warehouse security techniques, like data access control, may not be easy to impose and can be in effective. As an alternative, this paper proposes a data perturbation based approach, to provide privacy preserving in association rule mining on data cubes in a data warehouse. In order to conceal association rules and save the utility of transactions in data cubes, we select Genetic Algorithm to find optimum state of modification. In our approach various hiding styles are applied in different multi-objective fitness functions. To cope with the multi-objective functions, Pareto-front ranking strategy has been applied for obtaining the non-dominated solutions front. First objective of these functions is hiding sensitive rules and the second one is keeping the accuracy of transactions in data cube. After sanitization process we test the sanitization performance by evaluation of various criterions. The major feature is that the proposed strategy does not affect the functionality of the On-Line Analytical Processing system. Finally our experimental results show its effectiveness and feasibility.Keywords
OLAP, Data Cube, Data Mining, Association Rule HidingFull Text
References
- Chaudhuri S, Dayal U (1997) An Overview of Data Warehousing and OLAP Technology. Sigmod Record.
- Rizvi S J and Haritsa J R (2002) Maintaining Data Privacy in Association Rule Mining. In proceedings 28th VLDB Conference, Hong Kong, China.
- Verykios V, Elmagarmid A and Bertino E (2004) Association rule hiding. IEEE Transactions on Knowledge and Data Engineering, 16(4):434–447.
- Clifton C and Marks D (1996) Security and privacy implications of data mining. SIGMOD ’96: Proceedings of the 2000 ACM IGMOD International Conference on Management of Data, pages 15–20.
- Oliveira S and Zaiane O (2002) Privacy preserving frequent itemset mining. RPITS’14: Proceedings of the IEEE International Conference on Privacy, Security, and DataMining, pages 43–54.
- Sun X and Yu P S (2005) A border-based approach for hiding sensitive frequent itemsets. ICDM ’05: Proceedings of the 5th IEEE International Conference on Data Mining, pages 426-433.
- Atallah M, Bertino E (1999) A. Elmagarmid,M. Ibrahim and V. Verykios. Disclosure limitation of sensitive rules. Proc. of IEEE Knowledge and Data Engineering Exchange Workshop (KDEX).
- Verykios V, Elmagarmid A, Bertino E, Saygin Y and Dasseni E (2004) Association Rule Hiding. IEEE Trans. on Knowledge and Data Engineering, 16(4).
- Oliveira S and Zaiane O (2002) Privacy preserving frequent itemset mining. CRPITS’14: Proceedings of the IEEE International Conference on Privacy, Security, and Data Mining, pages 43–54.
- David L (1991) Handbook of Genetic Algorithms. New York : Van Nostrand Reinhold.
- Goldberg D E (1989) Genetic Algorithms: in Search, Optimization, and Machine Learning. New York : Addison-Wesley Publishing Co. Inc.
- Goldberg D, Karp B, Ke Y, Nath S, and Seshan S (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
- Kim I Y and Weck O L de (2005) Adaptive weightedsum method for bi-objective optimization: Pareto front generation. Struct Multidisc Optim. 29, 149–158, Springer.
- Amiri (2007) Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems, 43(1):181–191.
- Wang K, Fung B C M, and Yu P S (2005) Templatebased privacy preservation in classification problems. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM 2005), pages 466–473.
- Wang S L and Jafari A (2005) Using unknowns for hiding sensitive predictive association rules. In Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration (IRI 2005), pages 223–228.
- Wu X, Wu Y, Wang Y, and Li Y (2005) Privacy aware market basket data set generation: A feasible approach for inverse frequent set mining. In Proceedings of the 2005 SIAM International Conference on Data Mining (SDM 2005).
- Wu Y H, Chiang C M, and Chen A L P (2007) Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering, 19(1):29–42.
- Abul O, Atzori M, Bonchi F, and Giannotti F (2006) Hiding sequences. Technical report, Pisa KDD Laboratory, ISTI-CNR, Area della Ricerca di Pisa.
- Gkoulalas-Divanis and Verykios V (2006) An integer programming approach for frequent itemset hiding. In Proceedings of the 2006 ACM Conference on Information and Knowledge Management (CIKM 2006), pages 748–757.
- Inan and Saygin Y (2006) Privacy preserving spatiotemporal clustering on horizontally partitioned data. In Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006), pages 459–468.
- Jagannathan G, Pillaipakkamnatt K, and Wright R N (2006) A new privacy preserving distributed k-clustering algorithm. In Proceedings of the 2006 SIAM International Conference on Data Mining (SDM 2006), 2006.
- Wang L, Wijesekera D (2002) Cardinality-based Inference Control in Sum-only Data Cubes. Proc. of the 7th European Symp. on Research in Computer Security.
- Wang L, Li Y, Wijesekera D and Jajodia S (2003) Precisely Answering Multi-dimensional Range Queries without Privacy Breaches. ESORICS 2003, pages 100-115.
- Wang L, Jajodia S and Wijesekera D (2004) Securing OLAP data cubes against privacy breaches. Proc. IEEE Symp. on Security and Privacy, pages 161-175.
- Improving Performance of Search Engines Based on Fuzzy Classification
Abstract Views :464 |
PDF Views:95
Authors
Affiliations
1 Graduate Student of PNU University, Tehran, IR
2 Department of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, IR
3 Faculty of New Sciences and Technologies University of Tehran, Tehran, IR
1 Graduate Student of PNU University, Tehran, IR
2 Department of Computer Engineering, Najafabad branch, Islamic Azad University, Isfahan, IR
3 Faculty of New Sciences and Technologies University of Tehran, Tehran, IR
Source
Indian Journal of Science and Technology, Vol 5, No 11 (2012), Pagination: 3607-3611Abstract
At first glance, the service search-engine seems very useful and faultless, but by the more careful examination one may notice weaknesses in this search results. One of these weakness is that the result pages, which the search-engines offer is sometimes without content and sometimes have no relevance to the field that user had in mind . On the other hand many of quality-pages have no place in the search results. This paper advises search-engines to hand the job of decision making about the content of web sites to users, because humans are very much faster and have a very lower rate of error and can decide about the usefulness of a website with more justice. In the proposed algorithm which is based on fuzzy logic, we try to use parameters such as speed of mouse movements, scrolling speed, standard deviation of horizontal position of mouse and the time spent by user in each page to evaluate the extent of user's satisfaction with the page content. This ppaer describes the surveys conducted and then analyzes of the fuzzy variables, fuzzy sets and membership functions. Finally, discusses the benefits of the proposed algorithm.Keywords
Search Engines, Fuzzy Logic, Crawler, SEOFull Text
References
- Sean Odom, Lynell Allison (2011). SEO For 2012: Search Engine Optimization Secrets. s.l. : MediaWorks. 0984860002 9780984860005.
- Zhicheng Dou, Ruihua Song, Jian-Yun Nie, Ji-Rong Wen (2009). Using anchor texts with their hyperlink structure for web search. s.l. : SIGIR ‘09 Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval.
- Wen-Hsiang Lu, Lee-Feng Chien, Hsi-Jian Lee (2004). Anchor text mining for translation of Web queries: A transitive translation approach. s.l. : ACM Transactions on Information Systems (TOIS).
- Kevin Haas, Peter Mika, Paul Tarjan, Roi Blanco (2011). Enhanced results for web search. s.l. : SIGIR ‘11 Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval.
- Gaston L’Huillier, Hector Alvarez, Sebastián A. Ríos, Felipe Aguilera (2011). Topic-based social network analysis for virtual communities of interests in the dark web. s.l. : ACM SIGKDD Explorations Newsletter.
- Ricardo Baeza-Yates, Andrei Z. Broder, Yoelle Maarek (2011). The new frontier of web search technology: seven challenges. s.l. : Search computing.
- Eugene Agichtein, Zijian Zheng (2006). Identifying “best bet” web search results by mining past user behavior. s.l. : KDD ‘06 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining.
- Olivier Chapelle, Ya Zhang (2009). A dynamic bayesian network click model for web search ranking. s.l. : WWW ‘09 Proceedings of the 18th international conference on World wide web.
- Jeff Huang, Ryen W. White, Susan Dumais (2011). No clicks, no problem: using cursor movements to understand and improve search. s.l. : CHI ‘11 Proceedings of the 2011 annual conference on Human factors in computing systems.
- Cooke, Lynne (2006). Is the Mouse a “Poor man’s Eye Tracker”? s.l. : Usability and Information Design.
- Qi Guo, Eugene Agichtein (2010). Towards predicting web searcher gaze position from mouse movements. s.l. : CHI EA ‘10 Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems.
- Florian Mueller, Andrea Lockerd (2001). Tracking Mouse Movement Activity on WebSItes a Tool for User Modeling. s.l. : CHI EA ‘01 CHI ‘01 Extended abstracts on Human factors in computing systems.
- Kerry Rodden, Xin Fu (2007). exploring How Mouse Movements Relate to Eye Movements on Web Search Results Pages. s.l. : wisi workshop.
- Hao Ma, Raman Chandrasekar,Chris Quirk,Abhishek Gupta (2009). Improving Search Engines Using Human Computation Games. s.l. : Proceedings of the 18th ACM conference on Information and knowledge management.
- Uzun, Erdinç (2012). A fuzzy ranking approach for improving search results in Turkish as an agglutinative language. s.l. : Expert Systems with Applications: An International Journal.
- Hiding Sensitive Association Rules by Elimination Selective Item among R.h.s Items for each Selective Transaction
Abstract Views :276 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Engineering, Islamic Azad University–Najafabad Branch, IR
1 Department of Computer Engineering, Islamic Azad University–Najafabad Branch, IR
Source
Indian Journal of Science and Technology, Vol 7, No 6 (2014), Pagination: 831-837Abstract
This paper focuses on hiding sensitive association rule which is an important research problem in privacy preserving data mining. For this, we present an algorithm that decreases confidence of sensitive rules to below minimum threshold by removing selective item among items of consequent sensitive rule (R.H.S) for each selective transaction. Finally, we qualitatively compare the efficiency of the proposed algorithm with that of already published algorithms in hiding association rules.Keywords
Association Rule Minig, Datamining, Privacy PreservingFull Text