A Survey on Data Anonymization for Big Data Security
Author(s):
Athiramol. S , ST. JOSEPH'S COLLEGE OF ENGINEERING AND TECHNOLOGY PALAI; Sarju. S, ST. JOSEPH'S COLLEGE OF ENGINEERING AND TECHNOLOGY PALAIKeywords:
Anonymization, Cryptography, l-Diversity, Multi Set based Generalization, Semantic Anonymization, Taxonomy TreeAbstract:
Nowadays data analysis centers have a vital role in producing results that are beneficial for the society such as awareness about new disease outbreaks, the geographical areas affected by that disease, which aged people is mostly infected by that disease etc. The approach for protecting individual’s privacy from attackers are well known as anonymization. The word anonymization in this context is hiding the information in such a way that illegitimate user should not be able to infer anything while legitimate user such as an analyzer should get sufficient information from it. That is the anonymization is stated in terms of security and information loss. There are different techniques used for anonymization. In this review, different anonymization techniques and their disadvantages are discussed. The main motto of all such anonymization is low information loss and better security. Although providing 100 percent security and 100 percent data utility is not possible for any systems as anyone of them compromises accordingly. All the techniques are based on concepts.
Other Details:
| Manuscript Id | : | J4RV3I1035 |
| Published in | : | Volume : 3, Issue : 1 |
| Publication Date | : | 01/04/2017 |
| Page(s) | : | 88-91 |
