Call For Paper Volume:4 Issue:8 Aug'2017 |

BIG DATA PROCESSING WITH PRIVACY PRESERVING USING MAP REDUCE ON CLOUD

Publication Date : 15/06/2015



Author(s) :

Kaushlendra Singh Parihar , Rakesh Pratap Singh , Uttam Kumar , Mysore Jayakrishna Yogesh.


Volume/Issue :
Volume 2
,
Issue 6
(06 - 2015)



Abstract :

 A large number  of  cloud  services  require  users  to  share private data like electronic  health records for data  analysis  or  mining,  bringing  privacy  concerns. Anonymizing data sets via  generalization to satisfy certain privacy requirements such as k-anonymity is a widely used category  of  privacy  preserving  techniques.  At present,  the  scale  of  data  in  many  cloud applications   increases  tremendously  in accordance with the Big Data trend, thereby making it  a  challenge  for   commonly  used  software  tools   to  capture, manage, and  process  such large-scale data within a  tolerable elapsed time. As  a  result,  it  is  a  challenge  for existing anonymization  approaches  to   achieve  privacy preservation on privacy-sensitive large-scale data sets due  to  their  insufficiency   of    scalability.  In  this  paper,  we  propose  a  scalable  two-phase  top-down  specialization  (TDS)  approach  to anonymize  large-scale data sets  using  the Map-Reduce  framework on cloud. In both  phases  of  our  approach,  we  deliberately  design  a  group  of  innovative      Map-Reduce      jobs      to    concretely      accomplish      the  specialization  computation  in  a  highly  scalable  way.  Experimental  evaluation  results  demonstrate that with our  approach,  the  scalability  and   efficiency  of  TDS   can  be significantly   improved  over existing  approaches. 


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BIG DATA PROCESSING WITH PRIVACY PRESERVING USING MAP REDUCE ON CLOUD

June 13, 2015