scholarly journals Privacy Preserving Multi-server k-means Computation over Horizontally Partitioned Data

2018 ◽  
pp. 189-208
Author(s):  
Riddhi Ghosal ◽  
Sanjit Chatterjee
Keyword(s):  
Privacy Preserving ◽  
Partitioned Data ◽  
Multi Server

Privacy-preserving kriging interpolation on partitioned data

2014 ◽  
Vol 62 ◽  
pp. 38-46 ◽  
Author(s):  
Bulent Tugrul ◽  
Huseyin Polat
Keyword(s):  
Privacy Preserving ◽  
Kriging Interpolation ◽  
Partitioned Data

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.

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