Data Mining Based Privacy Attack Through Paper Traces

Privacy can be revealed from many sources, since data is available in papers and digital forms. Many techniques available to infer some private data based on these available sources, for example, data mining techniques. In this paper, it is investigated whether it is possible to derive some private information from shopping receipts, which are paper traces of people activities. Since people do not think that shopping receipts may harm them, usually they do not keep these shopping receipts safely. The techniques used in this research are data mining techniques; classification and clustering algorithm. In this first stage of research, the aim of this research is to determine whether it is possible to classify shopping data to correct shopper profiles, and to separate a set of shopping data into two meaningful groups, according to the shopping profiles. From the results, it can be concluded that it is possible to predict to which profile the data belongs to, with the percentage of data correctly classified higher than 97%. It also can be concluded that it is possible to separate the shopping receipts data into two groups, where each group represents each profile.