Abstract
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.
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Virupaksha, S., Gavini, D., Venkatesulu, D. (2017). Data Privacy in Online Shopping. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Computer Communication, Networking and Internet Security. Lecture Notes in Networks and Systems, vol 5. Springer, Singapore. https://doi.org/10.1007/978-981-10-3226-4_19
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DOI: https://doi.org/10.1007/978-981-10-3226-4_19
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