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.
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity Apr 20, 2007 Data Anonymisation and L-Diversity – Information with Insight Mar 12, 2019 CiteSeerX — l-Diversity: Privacy Beyond k-Anonymity
Aug 23, 2007
l-diversity, because reidentification doesn't tell the On privacy, research, and privacy research. l-diversity, because reidentification doesn't tell the whole story 2018-02-19 Where does privacy risk come from, when releasing anonymized data? What exactly can go wrong? Sweeney provided the first obvious answer: privacy risk appears when you can reidentify a … k-anonymity and l-diversity | History of an Idea: Missing Data
The main goal of CLDPP model is to protect the data. The CLDPP model is designed Clustering based l -Diversity Algorithm to group similar data together with l-diverse sensitive values and then anonymizes each group individually with aiming at improving privacy preservation rate.
Bibliographic details on An enhanced l-diversity privacy preservation. Add a list of references from and to record detail pages.. load references from crossref.org and opencitations.net t-Closeness: Privacy Beyond k-Anonymity and -Diversity To address these limitations ofk-anonymity, Machanavajjhala et al. introduced -diversity as a stronger notion of privacy. Deﬁnition 1 (The -diversity Principle)An equivalence class is said to have -diversity if there are at least “well-represented” values for the sensitive attribute. t-Closeness: Privacy Beyond k-Anonymity and l-Diversity Apr 20, 2007 Data Anonymisation and L-Diversity – Information with Insight