Privacy-preserving data publishing: Challenges and Solutions

This lecture delves into the challenges of privacy-preserving data publishing, exploring concepts like k-anonymity, l-diversity, and t-closeness. The instructor discusses real-life examples, such as the Netflix dataset, to illustrate the limitations of de-identification techniques. The lecture also covers the use of synthetic data as a potential solution to privacy concerns, highlighting its advantages and shortcomings. By the end, the audience gains insights into the complexities of balancing data utility with privacy protection in the era of big data.