Paper Title
A Greedy Generalized Heuristic Approach to Protect Identity Disclosure

The entire process of reconstructing original values from anonymized values could be turned away using a number of random values varying from 1 to 4 levels. This is often called as negative understanding. The negative understanding implementation prior systems is really a hypothesis and doesn't offer any evidential truth on its influence over k(m,n)-anonymization procedure. To sustain the efficiency of k(m,n) anonymization procedure, we attempt to demonstrate the hypothesis using real-time implementation. The paper defines k(mn)-anonymity, which supplies protection against identity disclosure and proposes a greedy anonymization heuristic that has the capacity to sanitize large datasets. The formula and the caliber of the anonymization are evaluated experimentally. Collections of real-world data will often have implicit or explicit structural relations. Within this work, we concentrate on tree structured data. Such data originate from various programs, even if your structure isn't directly reflected within the syntax, e.g. XML documents. An attribute situation is really a database where details about an individual is scattered among different tables which are connected through foreign keys. Keywords— Privacy, Tree Data, Anonymity, Structural Knowledge, Generalization, Disassociation