K-anonymity and Quasi-identifier

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between K-anonymity and Quasi-identifier

K-anonymity vs. Quasi-identifier

k-anonymity is a property possessed by certain anonymized data. Quasi-identifiers are pieces of information that are not of themselves unique identifiers, but are sufficiently well correlated with an entity that they can be combined with other quasi-identifiers to create a unique identifier.

Similarities between K-anonymity and Quasi-identifier

K-anonymity and Quasi-identifier have 2 things in common (in Unionpedia): Differential privacy, Latanya Sweeney.

Differential privacy

In cryptography, differential privacy aims to provide means to maximize the accuracy of queries from statistical databases while minimizing the chances of identifying its records.

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Latanya Sweeney

Latanya Arvette Sweeney is a Professor of Government and Technology in Residence at Harvard University, the Director of the Data Privacy Lab in the Institute of Quantitative Social Science (IQSS) at Harvard, and the Faculty Dean in Currier House at Harvard.

K-anonymity and Latanya Sweeney · Latanya Sweeney and Quasi-identifier · See more »

The list above answers the following questions

What K-anonymity and Quasi-identifier have in common
What are the similarities between K-anonymity and Quasi-identifier

K-anonymity and Quasi-identifier Comparison

K-anonymity has 26 relations, while Quasi-identifier has 8. As they have in common 2, the Jaccard index is 5.88% = 2 / (26 + 8).

References

This article shows the relationship between K-anonymity and Quasi-identifier. To access each article from which the information was extracted, please visit:

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