Data Privacy in Online Shopping

  • Shashidhar Virupaksha
  • Divya Gavini
  • D. Venkatesulu
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)


The Online Shopping experience has provided the new ways of business and shopping. Now the traditional way of shopping has changed into easy and convenience manner according to customer shopping behavior and preferences. Extracting shopping patterns from increasing data is not a trivial task. This paper will help to understand the importance of data mining techniques i.e., Association rule mining is to get relationships between different items in the dataset, and frequent item set mining aims to find the regularities in the shopping behavior of customers, clustering and concept hierarchy to provide business intelligence to improve sales, marketing and consumers satisfaction. In this paper while using data mining techniques there is data susceptibility, which is influenced by attacks like membership disclosure protection and homogeneity attack. These attacks deal with reveal of information based on quasi identifier value in the data set. In this paper, protecting sensitive information is an important problem. Detailed analysis of these both attacks are given and proposed a privacy definition called L-Diversity, which can be implemented and experimental evaluation is also shown.


Homogeneity attack K-Anonymity L-Diversity Dempster’s rule Apriori algorithm FP growth Quantitative association rule 


  1. 1.
    H. H. Aly, A. A. Amr, and Y. Taha, “Fast Mining of Association Rules in Large-Scale Problems,” Proc. IEEE Symp. Computers and Comm. (ISCC “01), pp. 107–113, 2001.Google Scholar
  2. 2.
    Kasun Wickramaratna, Miroslav Kubat and Kamal Premaratne, “Predicting Missing Items in Shopping Carts”, IEEE Trans. Knowledge and Data Eng., vol. 21, no. 7, july 2009.Google Scholar
  3. 3.
    P. Bollmann-Sdorra, A. Hafez, and V. V. Raghavan, “A Theoretical Framework for Association Mining Based on the Boolean Retrieval Model,” Data Warehousing and Knowledge Discovery: Proc. Third Int‟l Conf. (DaWaK‟01), pp. 21–30, Sept. 2001.Google Scholar
  4. 4.
    W. Li, J. Han, and J. Pei, “CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules,” Proc. IEEE Int‟l Conf. Data Mining (ICDM ‟01), pp. 369–376, Nov./Dec. 2001.Google Scholar
  5. 5.
    Quinlan, J. (1986), “Induction of Decision Trees,” Machine Learning, vol. 1, pp. 81–106.Google Scholar
  6. 6.
    XHEMALI, Daniela and J. HINDE, Christopher and G. STONE, Naive Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages, IJCSI International Journal of Computer Science Issues, Vol. 4, No. 1, 2009.Google Scholar
  7. 7.
    A. Sopharak, K. Thet Nwe, Y. Aye Moe, Matthew N. Dailey and B. Uyyanonvara, “Automatic Exudates Detection with a Naive Bayes Classifier”, Proceedings of the International Conference on Embedded Systems and Intelligent Technology, pp. 139–142, February 27–29, 2008.Google Scholar
  8. 8.
    E. W. T. Ngai, Li Xiu and D. C. K. Chau, 2009, Application of data mining techniques in customer relationship management: A literature review and classification, Expert Systems with Applications, Vol 36, Issue 2, Part 2, pp 2592–2602.Google Scholar
  9. 9.
    Magdalena G, T. Lasota, B. Trawiński “Comparative Analysis of Premises Valuation Models Using KEEL, RapidMiner, and WEKA” First International Conference, ICCCI, Wrocław, Poland, Oct 5–7, 2009. Proc., pp 800–812.Google Scholar
  10. 10.
    Tibshirani, R., Walther, G., and Hastie, T, 2001, estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society. Series B, Statistical methodology, 63:411–423.Google Scholar
  11. 11.
    G L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10(5):557–570, 2002.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Shashidhar Virupaksha
    • 1
    • 2
  • Divya Gavini
    • 3
  • D. Venkatesulu
    • 4
  1. 1.IT DeptartmenVRSECVijayawadaIndia
  2. 2.CSE DeptartmenVignan UniversityGunturIndia
  3. 3.Department of Computer Science and EngineeringVignan’s Lara Institute of Technology and ScienceGunturIndia
  4. 4.Department of Computer Science and EngineeringVignan UniversityGunturIndia

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