Mining out underlying knowledge from anonymity through collaborative efforts of data miners
S. Anil Kumar · 2016
Information is becoming the most precious resource. Mining out implied information from unstructured data has become essential in several project situations, and this itself is a complex process. Anonymity in data makes the data mining process much more difficult. Data Anonymization techniques, as being used in areas such as Cryptography and Cloud computing, aim to limit the possibilities of unnecessary data mining activities. It is being done by concealing, suppressing or generalizing the parts of information which is irrelevant to each of the individual stakeholders. However, collaboration of such stakeholders who are provided with different versions of anonymized data can reveal the anonymity up to a certain extent. This is an analytical study on such a possibility illustrated with the example of solving an anonymization puzzle involving two different persons who are trying to mine out the suppressed information and concealed knowledge through a collaborative approach in knowledge mining. This study points out the scope for a model driven approach in problem solving, and also the necessity of taking precautionary measures by the practitioners in both Data Anonnymization and Data Mining to resist the mutual competition, based on the ultimate project requirements.