Privacy Preservation in Data Mining Through Combinational Logic Operations
G. Manikandan, R. Keerthinath, P. Arunprasad, Sudhir Dawra, R. Vijay Sai · 2026
The process of data mining plays a crucial role in the examination of extensive datasets. Data mining enables the extraction of meaningful patterns and insights that may be concealed within the information. Data mining is utilized in social networks, healthcare, and finance. It is important to remember that data mining raises moral questions and privacy concerns. Companies need to handle data safely and in line with the law and morality. Privacy-preserving data mining (PPDM) is the process of data mining that protects the privacy of sensitive information. PPDM techniques try to find a balance between protecting privacy and getting the most out of data. PPDM lets businesses safely look at sensitive data and get useful information. The primary objective of this study is to investigate the use of combinational circuit logic to generate the noise to perform data perturbation. Basic logic gates such as AND, OR, and NOT gates are used to generate noise. The performance of the suggested technique has been evaluated using the K-means Clustering method. The Bank Marketing dataset from the UCI repository is utilized to assess the error rate of the proposed projection techniques.