The Projection-Based Data Transformation Approach for Privacy Preservation in Data Mining

Abstract

Data mining is vital in analyzing large volumes of data to extract functional patterns and knowledge hidden within the data. Data mining has practical applications in various scientific areas, such as social networks, healthcare, and finance. It is important to note that data mining also raises ethical concerns and privacy considerations. Organizations must handle data responsibly, ensuring compliance with legal and ethical guidelines. Privacy-Preserving Data Mining (PPDM) refers to conducting data mining tasks while protecting the privacy of sensitive data. PPDM techniques aim to strike a balance between privacy protection and data utility. By employing PPDM techniques, organizations can perform safe and private data analysis, protecting sensitive information while deriving valuable insights from the data. The current paper uses geometric transformation-based projection techniques such as perspective projection, isometric projection, cabinet projection, and cavalier projection to protect data privacy and improve data utility. The suggested technique's performance was assessed with the K-means clustering technique. The UCI repository's Bank Marketing dataset was used to verify the error rate of the proposed projection techniques.