A novel privacy-preserving technique using steganography and L-diversity for multi-relational educational dataset

Educational data is available in today’s world in abundance; it can be leveraged to improve students’ performance based on their academic records and to predict their future performances. Data sharing without intruding the privacy of individuals is a major concern. The present work proposes an improved privacy preserving k-anonymization Cluster-based Algorithm for a multi-relational educational dataset. To overcome the limitations of k-Anonymization, anonymized data is l-diversified to protect sensitive data from attacks. Further, Text Steganography is applied to avoid similarity attacks on l-diversified data to provide the second layer of privacy. Since the utility of data is an important factor, it must be maintained along with privacy to get useful information from the analysis. A Loss Metric is used to find the distortion of k-anonymized data to evaluate the balance between privacy and utility. Earth’s mover distance has been calculated for l-diversified data with steganography and without steganography to validate the results. For experiment purposes, an educational dataset has been used and results are compared with the existing approaches available in the literature. Statistical analysis has also been performed to justify the results.