A Machine Learning Approach for Privacy-preservation in E-business Applications

This paper aims at identifying and presenting useful solutions to close the privacy gaps in some definite data mining tasks with three primary goals. The overarching aim is to keep efficiency and accuracy of data mining tasks that handle the operations while trying to improve privacy. Specifically, we demonstrate that a machine learning methodology is an appropriate choice to preserve privacy in big data. As core contribution we propose a model consisting of several representative efficient methods for privacy-preserving computations that can be used to support data mining. The planned outcomes and contributions of this paper will be a set of improved methods for privacy-preserving soft-computing based clustering in distributed environments for e-business applications. The proposed model demonstrates that soft computing methods can lead to novel results not only to promote the privacy protection, but also for retaining performance and accuracy of regular operations, especially in online business applications.

[1]  P. Gaur.  Neural networks in data mining , 2018 .

[2]  Gerald Quirchmayr,et al.  A comparative study on innovative approaches for privacy-preservation in knowledge discovery , 2017, ICIME 2017.

[3]  Laurence T. Yang,et al.  A privacy-preserving high-order neuro-fuzzy c-means algorithm with cloud computing , 2017, Neurocomputing.

[4]  Mamta Narwaria,et al.  Privacy preserving data mining — ‘A state of the art’ , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[5]  M. B. Malik,et al.  Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects , 2012, 2012 Third International Conference on Computer and Communication Technology.

[6]  Ali Miri,et al.  Privacy-preserving back-propagation and extreme learning machine algorithms , 2012, Data Knowl. Eng..

[7]  Ryan Williams,et al.  Resolving the Complexity of Some Data Privacy Problems , 2010, ICALP.

[8]  F.L. Gorgonio,et al.  Combining Parallel Self-Organizing Maps and K-Means to Cluster Distributed Data , 2008, 2008 11th IEEE International Conference on Computational Science and Engineering - Workshops.

[9]  Lior Rokach,et al.  Soft Computing for Knowledge Discovery and Data Mining , 2007 .

[10]  Shuguo Han,et al.  Privacy-Preserving Self-Organizing Map , 2007, DaWaK.

[11]  Yunfeng Wang,et al.  Privacy Preserving Data Mining Research: Current Status and Key Issues , 2007, International Conference on Computational Science.

[12]  Martin Bichler,et al.  Design science in information systems research , 2006, Wirtschaftsinf..

[13]  Hsinchun Chen,et al.  Intelligence and security informatics: information systems perspective , 2006, Decis. Support Syst..

[14]  Bart Goethals,et al.  On Private Scalar Product Computation for Privacy-Preserving Data Mining , 2004, ICISC.

[15]  Elisa Bertino,et al.  Association rule hiding , 2004, IEEE Transactions on Knowledge and Data Engineering.

[16]  Elisa Bertino,et al.  State-of-the-art in privacy preserving data mining , 2004, SGMD.

[17]  Kyong Joo Oh,et al.  The collaborative filtering recommendation based on SOM cluster-indexing CBR , 2003, Expert Syst. Appl..

[18]  Shashank Pushkar,et al.  Fuzzy-Based Privacy Preserving Approach in Centralized Database Environment , 2017 .

[19]  C. V. Guru Rao,et al.  A Comparative Study of Data Perturbation Using Fuzzy Logic to Preserve Privacy , 2014 .

[20]  Philip S. Yu,et al.  A General Survey of Privacy-Preserving Data Mining Models and Algorithms , 2008, Privacy-Preserving Data Mining.

[21]  S. Mingoti,et al.  Stochastics and Statistics Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms , 2006 .

[22]  Padhraic Smyth,et al.  Advances in Knowledge Discovery and Data Mining , 2006, Lecture Notes in Computer Science.

[23]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[24]  Pabitra Mitra,et al.  Data mining in soft computing framework: a survey , 2002, IEEE Trans. Neural Networks.