Privacy-Preserving Record Linkage via Bilinear Pairing Approach

• Main Authors: Chih-Hsun Lin, 林志訓
• Other Authors: 游家牧
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/8eg367

碩士 === 國立中興大學 === 資訊科學與工程學系 === 106 === In the era of big data, people are increasingly focusing on the useful information of various sources and looking for potential relation hidden in the data. Privacy-preserving record linkage (PPRL) is a means for finding the correspondence of records from different datasets with the guarantee of no privacy leakage from individuals. Here, we propose a simple yet effective PPRL protocol as a platform for the information mining in the real world. We perform an implementation to test the feasibility and efficiency of our proposed protocol. Besides, due to the property of the bilinear pairing that does not contain the non-deterministic mathematical formula. We modify the mathematical algorithm to fit the GPU implementation, that is, to reduce the frequency of the branch instruction in the division and quadratic residue.