Often k-anonymity may not be the best tool for privacy. In this video, learn how you can use l-diversity to improve not just privacy but also data quality.
Privacy Preservation - an overview | ScienceDirect Topics Anonymization model: There are different types of privacy models proposed for preserving privacy of data, such as K-anonymity, L-diversity, and (α, k)-anonymity. K -anonymity maintains the data authenticity [13] , where the values of quasi-identifier (QI) attributes (age, zip, gender) in each tuple in the table are matched to at least ( K Big healthcare data: preserving security and privacy Jan 09, 2018 K-anonymity versus l-diversity
Data Anonymisation and L-Diversity – Information with Insight
The l-diversity approach is insufficient to prevent sensitive attribute disclosure This led to the proposal of another privacy definition called t-Closeness t-Closeness achieves privacy by keeping the distribution of each quasi-identifier’s sensitive attribute “close” to their distribution in the database Research on Trajectory Data Releasing Method via
Apart from this minor change, the only difference with the original Incognito algorithm is the different privacy definition (i.e., various l-diversity instantiations). Field Summary; private LatticeManager: man Lattice manager that controls how the generalization lattice is traversed .
Research on Trajectory Data Releasing Method via