Project Login
Registration No:
Password:
MAIL ALERTS SMS ALERTS
 
     
   
     

Slicing A New Approach to Privacy Preserving Data Publishing.

Platform : DATA MINING

IEEE Projects Years : 2012

Slicing A New Approach to Privacy Preserving Data Publishing.

ABSTRACT
Several anonymization techniques, such as generalization
and bucketization, have been designed for privacy preserving
microdata publishing. Recent work has shown that general-
ization loses considerable amount of information, especially
for high-dimensional data. Bucketization, on the other hand,
does not prevent membership disclosure and does not apply
for data that do not have a clear separation between quasi-
identifying attributes and sensitive attributes.
In this paper, we present a novel technique called slicing,
which partitions the data both horizontally and vertically.
We show that slicing preserves better data utility than gen-
eralization and can be used for membership disclosure pro-
tection. Another important advantage of slicing is that it
can handle high-dimensional data. We show how slicing can
be used for attribute disclosure protection and develop an ef-
ficient algorithm for computing the sliced data that obey the
â„“-diversity requirement. Our workload experiments confirm
that slicing preserves better utility than generalization and
is more effective than bucketization in workloads involving
the sensitive attribute. Our experiments also demonstrate
that slicing can be used to prevent membership disclosure.


NOW GET PROJECTS ! GET TRAINED ! GET PLACED !

IEEE, NON-IEEE, REAL TIME LIVE ACADEMIC PROJECTS,

PROJECTS WITH COMPLETE COURSES,SOFT SKILLS & PLACEMENTS

ALLOVER INDIA & WORLD WIDE,

HOSTEL FACILITY AVAILABLE FOR GIRLS & BOYS SEPARATELY,

CALL: 08985129129 ,  E-Mail Id: support@ascentit.in

REGISTER FOR PROJECTS NOW ! GET DISCOUNT
   
1