scholarly journals Privacy Preserving Combinatorial Function for Multi-Partitioned Data Sets

2012 ◽  
Vol 44 (8) ◽  
pp. 8-10
Author(s):  
V. S.Prakash ◽  
A. Shanmugam ◽  
P. Murugesan
Keyword(s):  
Privacy Preserving ◽  
Data Sets ◽  
Partitioned Data ◽  
Combinatorial Function

scholarly journals Optimization through Bayesian Classification on the k-Anonymized Data

2012 ◽  
pp. 245-249
Author(s):  
L Mohana Tirumala ◽  
S. Srinivasa Rao
Keyword(s):  
Cost Effective ◽  
Privacy Preserving ◽  
Bayesian Classification ◽  
Data Sets ◽  
Data Set ◽  
Equal Importance ◽  
Cost Effective Approach ◽  
Effective Manner ◽  
Anonymized Data ◽  
Refinement Algorithm

Privacy preserving in Data mining & publishing, plays a major role in today networked world. It is important to preserve the privacy of the vital information corresponding to a data set. This process can be achieved by k-anonymization solution for classification. Along with the privacy preserving using anonymization, yielding the optimized data sets is also of equal importance with a cost effective approach. In this paper Top-Down Refinement algorithm has been proposed which yields optimum results in a cost effective manner. Bayesian Classification has been proposed in this paper to predict class membership probabilities for a data tuple for which the associated class label is unknown.

PRIVACY-PRESERVING RANDOM PROJECTION-BASED RECOMMENDATIONS BASED ON DISTRIBUTED DATA

2013 ◽  
Vol 12 (02) ◽  
pp. 201-232 ◽  
Author(s):  
CIHAN KALELI ◽  
HUSEYIN POLAT
Keyword(s):  
Real Data ◽  
Cold Start ◽  
Random Projection ◽  
Privacy Preserving ◽  
Distributed Data ◽  
Legal Regulations ◽  
Privacy Concerns ◽  
Private Data ◽  
Partitioned Data ◽  
Limited Coverage

Providing recommendations based on distributed data has received an increasing amount of attention because it offers several advantages. Online vendors who face problems caused by a limited amount of available data want to offer predictions based on distributed data collaboratively because they can surmount problems such as cold start, limited coverage, and unsatisfactory accuracy through partnerships. It is relatively easy to produce referrals based on distributed data when privacy is not a concern. However, concerns regarding the protection of private data, financial fears due to revealing valuable assets, and legal regulations imposed by various organizations prevent companies from forming collaborations. In this study, we propose to use random projection to protect online vendors' privacy while still providing accurate predictions from distributed data without sacrificing online performance. We utilize random projection to eliminate the aforementioned issues so vendors can work in partnerships. We suggest privacy-preserving schemes to offer recommendations based on vertically or horizontally partitioned data among multiple companies. The recommended methods are analyzed in terms of confidentiality. We also analyze the superfluous loads caused by privacy concerns. Finally, we perform real data-based trials to evaluate the accuracy of the proposed schemes. The results of our analyses show that our methods preserve privacy, cause insignificant overheads, and offer accurate predictions.

Sign in / Sign up

Export Citation Format

Share Document