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An Inference Detection Algorithm Based on Related Tuples Mining

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3683))

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Abstract

Existing algorithms on inference detection for database systems mainly employ functional dependencies in the database schema to detect inference, but what they can detect is limited. This paper presents a new data level inference detection algorithm. It can determine whether sensitive information can be disclosed from the user’s query history through finding the related tuples between the return results of different queries. If two tuples are related to each other, then they will be merged into one tuple, thus the query history can be compressed. Moreover, the merged tuple has more information than the original two or more tuples. The experiment results show that, as the query number increases, our algorithm can infer almost the whole original relation; meanwhile the query history is compressed remarkablely. The system administrator should restrict user’s query number and category to ensure that the database is secure.

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Authors and Affiliations

  1. Computer Science and Technology Institute, Harbin Engineering University, Harbin, 150001, Heilongjiang, China

    Binge Cui & Daxin Liu

Authors
  1. Binge Cui
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  2. Daxin Liu
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Cui, B., Liu, D. (2005). An Inference Detection Algorithm Based on Related Tuples Mining. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_142

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  • DOI: https://doi.org/10.1007/11553939_142

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28896-1

  • Online ISBN: 978-3-540-31990-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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