Email this article A new differential private technique for frequent item mining
Abstract
Frequent itemsets mining with differential protection refers to the issue of mining all incessant itemsets whose bolsters are over a given limit in a given value-based dataset, with the imperative that the mined outcomes should not break the security of any single exchange. Current answers for this issue can't well adjust proficiency, security and information utility over vast scaled information. Toward this end, we propose a proficient, differential private incessant itemsets mining algorithm over vast scale information. In light of the thoughts of examining and exchange truncation utilizing length limitations, our algorithm decreases the algorithm force, diminishes mining affectability, and in this way improves information utility given a fixed protection spending plan.
References
Z. John Lu, “The elements of statistical learning: data mining, inference, and prediction,” Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 173, no. 3, pp. 693–694, 2010.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery in databases,” AI magazine, vol. 17, no. 3, p. 37,1996.
H. Yang, K. Huang, I. King, and M. R. Lyu, “Localized support vector regression for time series prediction,” Neuro computing, vol. 72, no. 10-12, pp. 2659–2669, 2009.
C. Romero and S. Ventura, “Educational data mining: A review of the state of the art,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, pp. 601–618, Nov 2010.
J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
X. Fang, Y. Xu, X. Li, Z. Lai, and W. K. Wong, “Robust semi-supervised subspace clustering via non-negative low-rank representation,” IEEE Transactions on Cybernetics, vol. 46, pp. 1828–1838, Aug 2016.
M. Pe˜na, F. Biscarri, J. I. Guerrero, I. Monedero, and C. Le´on, “Rulebasedsystem to detect energy efficiency anomalies in smart buildings, a data mining approach,” Expert Systems with Applications, vol. 56,pp. 242–255, 2016.
Y. Guo, F. Wang, B. Chen, and J. Xin, “Robust echo state networks based on correntropy induced loss function,” Neuro computing, vol. 267,pp. 295–303, 2017.
H. Lim and H.-J. Kim, “Item recommendation using tag emotion in social cataloging services,” Expert Systems with Applications, vol. 89,pp. 179–187, 2017.
R. C.-W. Wong, J. Li, A. W.-C. Fu, and K. Wang, “(_, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 754–759, ACM, 2006.
L. Sweeney, “k-anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, pp. 557–570, 2002.
S. Latanya, “Achieving k-anonymity privacy protection using generalization and suppression,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, no. 05, pp. 571–588, 2002.
A. Meyerson and R. Williams, “On the complexity of optimal kanonymity,” in Proceedings of the Twenty-third ACM SIGMOD-SIGACTSIGART Symposium on Principles of Database Systems, pp. 223–228,ACM, 2004.
Y. Zhang, J. Zhou, F. Chen, L. Y. Zhang, K. Wong, X. He, and D. Xiao, “Embedding cryptographic features in compressive sensing, ”Neuro computing, vol. 205, pp. 472–480, 2016.
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramani“l-diversity: Privacy beyond k-anonymity,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 1, no. 1, p. 3, 2007.
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