Mastering data privacy: leveraging K-anonymity for robust health data sharing

Abstract

AbstractIn modern healthcare systems, data sources are highly integrated, and the privacy challenges are becoming a paramount concern. Despite the critical importance of privacy preservation in safeguarding sensitive and private information across various domains, there is a notable deficiency of learning and training material for privacy preservation. In this research, we present a k-anonymity algorithm explicitly for educational purposes. The development of the k-anonymity algorithm is complemented by seven validation tests, that have also been used as a basis for constructing five learning scenarios on privacy preservation. The outcomes of this research provide a practical understanding of a well-known privacy preservation technique and extends the familiarity of k-anonymity and the fundamental concepts of privacy protection to a broader audience.