Bilgi sistemleri için gizliliği koruyan veri analizi

Data collection and processing progress made data mining a popular tool among organizations in the last decades. Sharing information between companies could make this tool more beneficial for each party. However, there is a risk of sensitive knowledge disclosure. Shared data should be modified in such a way that sensitive relationships would be hidden. Since the discovery of frequent itemsets is one of the most effective data mining tools that firms use, privacy-preserving techniques are necessary for continuing frequent itemset mining. There are two types of approaches in the algorithmic nature: heuristic and exact. This study presents an exact itemset hiding approach, which uses constraints for a better solution in terms of side effects and minimum distortion on the database. The proposed approach does not require frequent itemset mining executed prior to the hiding process. This gives our approach an advantage in total running time. We give an evaluation of our algorithm on some benchmark datasets. Our results show the effectiveness of our hiding approach and elimination of prior mining of itemsets is time efficient. In addition, we conducted a survey to understand the awareness of people regarding the sensitivity of their personal data. The results show that participants tend to protect their privacy whenever possible and have a different attitude of sensitivity in different situations. In addition, it has been observed that participants tend to give misleading information when they do not feel comfortable. This study shows that people are uncomfortable with sharing sensitive information with third parties rather than collecting it.