International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Sep. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 90
Paper Published : 1248
No. of Authors : 3136
  Journal Paper

Paper Title
A Greedy Generalized Heuristic Approach to Protect Identity Disclosure

Abstract
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


Author - Eleswarapu Navyasree, Repudi Ramesh

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| Published on 2017-02-16
   
   
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