Differential Privacy and Its Challenges: A Literature Review

Now a days educational data has been produced in large amount by learners. Educational data contains online as well as offline learning resources of individuals, learning experience, attendance record, assignment records and many other records. Some useful information can be extracted from this available data which can be further used in improving teaching practises and learning experience of learners. It can also help in increasing the success rate of students. Sharing and analysis of data introduces risk of privacy. This is the responsibility of data curator to provide privacy to individuals data. There are many exiting privacy-preserving algorithms which are used by researchers to sustain privacy of the data. Differential privacy is one of the popular privacy-preserving techniques which tries to reduce privacy leakage of data by adding noise to data. Differential privacy protects individuals’ information from attacker and also maintains accuracy of data. In the proposed work different techniques to implement differential privacy have been explored in detail with their comparative analysis. Different types of differential privacy, sequential decomposition and their comparison with other privacy-preserving techniques is also provided.