Mining frequent itemsets in the presence of malicious participants

Mining frequent itemsets in the presence of malicious participants

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Information Security — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Privacy preserving data mining (PPDM) algorithms attempt to reduce the injuries to privacy caused by malicious parties during the rule mining process. Usually, these algorithms are designed for the semi-honest model, where participants do not deviate from the protocol. However, in the real-world, malicious parties may attempt to obtain the secret values of other parties by probing attacks or collusion. In this study, the authors study how to preserve the privacy of participants in a collusion-free model of the frequent itemset mining process, where the protocol protects against probing attacks and collusion. The mining of frequent itemsets is the main step of association rule mining algorithms, and, in this study, the authors propose two privacy-preserving frequent itemset mining algorithms for both two-party and multi-party states in a collusion-free model for vertically partitioned (heterogeneous) data; in addition, a privacy measuring technique is proposed, which quantifies privacy based on the amount of disclosed sensitive information.


    1. 1)
      • F. Emekci , O.D. Sahisn , D. Agrawal , A. El abbadi . Privacy preserving decision tree learning over multiple parties. J. Data Knowl. Eng. , 348 - 361
    2. 2)
      • X. Yi , Y. Zhang . Privacy preserving distributed association rule mining via semi-trusted mixer. J. Data Knowl. Eng. , 550 - 567
    3. 3)
      • Agrawal, R., Srikant, R.: `Fast algorithms for mining association rules', Proc. Int. Conf. Very Large Databases, 1994, p. 487–499, Santiago, Chile.
    4. 4)
      • S. Zhong . Privacy preserving algorithm for distributed mining of frequent itemsets. J. Inform. Sci. , 490 - 503
    5. 5)
      • J. Han , M. Kamber . (2006) Data mining concepts and technologies.
    6. 6)
      • T. ElGamal . A public-key cryptosystem and a signature scheme based on discrete logarithms. J. IEEE Trans. Info. Theory , 469 - 472
    7. 7)
      • Boneh, D.: `The decision Diffie-Helman problem', Proc. ANTS3, 1998, p. 48–63, (LNCS, 1423).
    8. 8)
      • Agrawal, D., Srikant, R.: `Privacy preserving mining', Proc. ACMSIGMOD Conf. Management of Data, 2000, p. 439–450, Dallas, TX, USA.
    9. 9)
      • Vaida, J., Clifton, C.: `Privacy preserving association rule mining in vertically partitioned data', Proc. Eighth ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2002, p. 639–644, Edmonton, Alberta, Canada.
    10. 10)
      • Kantarcioglu, M., Clifton, C.: `Privacy preserving distributed mining of association rules on horizontally partitioned data', Proc. ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, p. 24–31, Madison, Wisconsin, USA, June 2002.
    11. 11)
    12. 12)
      • Yao, A.: `How to generate and exchange secrets', Proc. 27th IEEE Symp. Foundation of Computer Science, 1986, p. 162–167, Toronto, Canada.
    13. 13)
      • Geothals, B., Laur, S., Lipmaa, H., Mielikainen, T.: `On private scalar product for privacy-preserving data mining', Proc. Seventh Int. Conf. Information Security and Cryptography, 2005, p. 104–120, Seoul, South Korea.
    14. 14)
      • Wright, R.N., Yang, Z.: `Privacy preserving Bayesian network structure computation on distributed heterogeneous data', Proc. KDD'04, 2004, p. 713–718, Seattle, WA, USA.
    15. 15)
      • B. Rozenberg , ‘E. Gudes . Association rule mining in vertically partitioned databases. J. Data Knowl. Eng. , 378 - 396
    16. 16)
      • J. Zhan , S. Matwin , L. Chang . Privacy-preserving collaborative association rule mining. J. Network. Comp. Appl. , 1216 - 1227
    17. 17)
      • Du, W., Zhan, Z.: `Building decision tree classifier on private data', Proc. IEEE Int. Conf. Privacy, Security and Data Mining, 2002, p. 1–8, Maebashi, Japan.
    18. 18)
      • B. Shapira , Y. Elovici , A. Meshiach , T. Kuflik . PRAW – a privacy model for the web. J. Am. Soc. Inf. Sci. Tech. , 2 , 159 - 172
    19. 19)
      • I. Damgard , M. Geisler , M. Kroigard . Homomorphic encryption and secure comparison. Int. J. Appl. Cryptogr. , 1 , 22 - 31
    20. 20)
      • A. Inan , S. Kaya , Y. Saygin , E. Savas , A. Hintoglu , A. Levi . Privacy preserving clustering on horizontally partitioned data. J. Data Knowl. Eng. , 646 - 666

Related content

This is a required field
Please enter a valid email address