This reduction is a trade off that results in some loss of effectiveness of data management or mining algorithms in order to gain some privacy.
The l-diversity model handles some of the weaknesses in the k-anonymity model where protected identities to the level of k-individuals is not equivalent to protecting the corresponding sensitive values that were generalized or suppressed, especially when the sensitive values within a group exhibit homogeneity.
The l-diversity model adds the promotion of intra-group diversity for sensitive values in the anonymization mechanism.
While k-anonymity is a promising approach to take for group based anonymization given its simplicity and wide array of algorithms that perform it, it is however susceptible to many attacks.
The book Privacy-Preserving Data Mining – Models and Algorithms (2008)[1] defines l-diversity as being: Let a q*-block be a set of tuples such that its non-sensitive values generalize to q*.