Fouzia Sultana, Humera Shahab


Within previously mentioned plaster, we think about the issue of checking if the retainer got here subsidize proper and stop overrun itemset. There are plenty of you can causes of your association overhanging uphold in right kind solutions. Our intention will be to fashion skillful and robust purity credentials techniques to take such helper a certain could return improper and incomplete attend itemset. Our experiments show the effectiveness and efficiency in our concepts. The host performs haunt itemset mining around the received dataset and returns the mining leads to the customer. We permit the customer to make use of privacy-preserving play itemset mining algorithms. We optimize the testimony formula by reduction of your amount of reasons for right kindness and integrity evidence. The right kindness credentials in the customer part is easy. The customer uses the menial’s data to ensure the itemset the one in question every MPB node matches is haunt by running the set intersection averment protocol. A naïve method of certify the plenum of MPB nodes will be that one the customer re-computes MPB taken away FS, which can close boulevard of nearly while outlay. We form a much more active technique as suite. we eye the conduct of certification apprehension inside the host part and information in the customer view and explored numerous factors the one in question change up the scoop opera in our deterministic come, with a range of miscalculation rate, commonplace itemset of a variety of lengths, and a variety of table sizes.


Data Mining As A Service; Security; Result Integrity Verification; Integrity Verification; Datasets; Privacy-Preserving;


B. Parno, M. Raykova, and V. Vaikuntanathan, “How to delegate and verify in public: Verifiable computation from Attribute-based encryption,” in Proc. 9th Theory Cryptography Conf., 2012, pp. 422–439.

K.-T. Chuang, J.-L. Huang, and M.-S. Chen, “Power-law relationship and Self-similarity in the itemset support distribution: Analysis and applications,” VLDB J., vol. 17, pp. 1121–1141, Aug. 2008.

R. Liu, H. Wang, A. Monreale, D. Pedreschi, F. Giannotti, and WengeGuo, “Audio: An integrity auditing framework of Outliermining- as-a-service systems,” in Proc. Eur. Conf. Mach. Learning Knowl. Discovery Databases, 2012, pp. 1–18.

F. Giannotti, L. V. S. Lakshmanan, A. Monreale, D. Pedreschi, and W. Hui Wang, “Privacy-preserving data mining from outsourced databases,” in Proc. 3rd Int. Conf. Comput., Privacy Data Protection, 2011, pp. 411–426.

M. T. Goodrich, C. Papamanthou, D. Nguyen, R. Tamassia, C. V. Lopes, O. Ohrimenko, and N. Triandopoulos, “Efficient verification of web-content searching through authenticated web crawlers,” Proc. VLDB Endowment, vol. 5, pp. 920–931, 2012.

L. Babai, L. Fortnow, L. A. Levin, and M. Szegedy, “Checking computations in polylogarithmic time,” in Proc. 23rd Annu. ACM Symp. Theory Comput. 1991, pp. 21–32.

Full Text: PDF


  • There are currently no refbacks.

Copyright © 2012 - 2018, All rights reserved.| ijitr.com

Creative Commons License
International Journal of Innovative Technology and Research is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJITR , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.