scholarly journals Privacy Preserving Market Basket Data Analysis

2007 ◽  
pp. 103-114 ◽  
Author(s):  
Ling Guo ◽  
Songtao Guo ◽  
Xintao Wu
Keyword(s):  
Data Analysis ◽  
Privacy Preserving ◽  
Market Basket

scholarly journals Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

2019 ◽  
Vol 1 (1) ◽  
pp. 483-491 ◽  
Author(s):  
Makhamisa Senekane
Keyword(s):  
Support Vector Machine ◽  
Data Analysis ◽  
Image Classification ◽  
Differential Privacy ◽  
Privacy Preserving ◽  
Global Optimum ◽  
Support Vector ◽  
Sensitive Data ◽  
Radiological Images ◽  
Golden Standard

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

Privacy-Preserving Statistical Data Analysis on Federated Databases

2014 ◽  
pp. 30-55 ◽  
Author(s):  
Dan Bogdanov ◽  
Liina Kamm ◽  
Sven Laur ◽  
Pille Pruulmann-Vengerfeldt ◽  
Riivo Talviste ◽  
...  
Keyword(s):  
Data Analysis ◽  
Statistical Data ◽  
Privacy Preserving ◽  
Statistical Data Analysis ◽  
Federated Databases

scholarly journals Incentive Compatible Privacy-Preserving Data Analysis

2013 ◽  
Vol 25 (6) ◽  
pp. 1323-1335 ◽  
Author(s):  
Murat Kantarcioglu ◽  
Wei Jiang
Keyword(s):  
Data Analysis ◽  
Privacy Preserving ◽  
Incentive Compatible
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