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Privacy Aware Data Management and Chase

Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
One of the key applications that uses the knowledge discovered by data mining is called Chase. Chase is a process that replaces null or missing values with the values predicted by the knowledge, and it is mainly used to obtain more complete information systems or to replace unknown attribute values in user queries. The process improves the quality of query answers with increased volume of reliable data, and helps the system understand user queries that would otherwise be difficult. However, a security breach may occur when a set of data in an information system is confidential. The confidential data can be hidden from the public view. However, Chase has the capability to reveal the hidden data by classifying them as null or missing. In this paper, we discuss disclosure of confidential data by Chase and protection algorithms that reduce the risk. In particular, the proposed algorithms aim to protect confidential data with the least amount of additional data hiding.
Wydawca
Rocznik
Strony
507--524
Opis fizyczny
bibliogr. 23 poz., tab., wykr.
Twórcy
autor
  • Department of Computer Science, University of Pittsburg at Johnstown, Johnstown, PA 15904, USA, sim@pitt.edu
Bibliografia
  • [1] Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases, AddisonWesley, 1995.
  • [2] Clifton, C., Kantarcioglou, M., Lin, X., Zhu, M.: Tools for privacy preserving distributed data mining, SIGKDD Explorations, 2002.
  • [3] Dardzińska, A., Raś, Z.: Chasing Unknown Values in Incomplete Information Systems, Proceedings of ICDM 03 Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, November 2003.
  • [4] Dardzińska, A., Raś, Z.: On Rules Discovery from Incomplete Information Systems, Proceedings of ICDM 03 Workshop on Foundations and New Directions of Data Mining, Melbourne, Florida, November 2003.
  • [5] Dardzińska, A., Raś, Z.: Rule-Based Chase Algorithm for Partially Incomplete Information Systems, Proceedings of the Second International Workshop on Active Mining, Maebashi City, Japan, October 2003.
  • [6] Du,W., Zhan, Z.: Building decision tree classifier on private data, Proceedings of the IEEE ICDM Workshop on Privacy, Security and Data Mining, 2002.
  • [7] Guarino, N., Giaretta, P.: Ontologies and knowledge bases, towards a terminological clarification, Towards Very Large Knowledge Bases: Knowledge Building and Knowledge Sharing, 1995.
  • [8] Im, S., Raś, Z.: Ensuring Data Security against Knowledge Discovery in Distributed Information System, Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Regina, Canada, September 2005.
  • [9] Im, S., Raś, Z., Dardzińska, A.: Building A Security-Aware Query Answering System Based On Hierarchical Data Masking, Proceedings of the ICDM Workshop on Computational Intelligence in Data Mining, Huston, Texas, November 2005.
  • [10] Im, S., Raś, Z., Dardzińska, A.: SCIKD: Safeguring Classified Information agaist Knowledge Discovery, Proceedings of the ICDM Workshop on Foundations of Data Mining, Huston, Texas, November 2005.
  • [11] K., M., Sarathy, R.: A Theoretical Basis for Perturbation Methods, Statistics and Computing, 2003.
  • [12] Kantarcioglou, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data, Proceedings of the ACM SIGMOD Workshop on Research Isuues in Data Mining and Knowledge Discovery, 2002.
  • [13] Lindell, Y., Pinkas, B.: Privacy preserving data mining, Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology, Springer-Verlag, London, UK, 2000.
  • [14] Oliveira, S. R. M., Zaiane, O. R.: Privacy preserving frequent itemset mining, Proceedings of the IEEE ICDM Workshop on Privacy, Security and Data Mining, 2002.
  • [15] Pawlak, Z.: Rough sets-theoretical aspects of reasoning about data, Kluwer, 1991.
  • [16] Raś, Z.: Dictionaries in a distributed knowledge-based system, Concurrent Engineering: Research and Applications, Concurrent Technologies Corporation, Pittsburgh, Penn., 1994.
  • [17] Raś, Z., Dardzińska, A.: Ontology Based Distributed AutonomousKnowledge Systems, Information Systems International Journal, 29(1), 2004, 47-58.
  • [18] Raś, Z., Dardzińska, A.: Query Answering based on Collaboration and Chase, Proceedings of the 6th International Conference On Flexible Query Answering Systems, Lyon, France, June 2004.
  • [19] Raś, Z., Dardzińska, A.: CHASE-2: Rule based chase algorithm for information systems of type lambda, Proceedings of the Second International Workshop on Active Mining, Maebashi City, Japan, October 2005.
  • [20] Raś, Z., Dardzińska, A.: Extracting Rules from Incomplete Decision Systems: System ERID, 2005.
  • [21] S. Hettich, C. B., Merz, C.: UCI Repository of machine learning databases, 1998.
  • [22] Saygin, Y., Verykios, V., Elmagarmid, A.: Privacy preserving association rule mining, Proceedings of the 12th International Workshop on Research Issues in Data Engineering, 2002.
  • [23] Yao, A. C.: How to generate and exchange secrets, Proceedings of the 27th IEEE Symposium on Foundations of Computer Science, IEEE, 1996.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0010-0041
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