TY - GEN
T1 - Privacy-Preserving Record Linkage via Bilinear Pairing Approach
AU - Lin, Chih Hsun
AU - Yu, Chia Mu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85053869987&partnerID=8YFLogxK
U2 - 10.1109/ICCE-China.2018.8448454
DO - 10.1109/ICCE-China.2018.8448454
M3 - Conference contribution
AN - SCOPUS:85053869987
SN - 9781538663011
T3 - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
BT - 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018
Y2 - 19 May 2018 through 21 May 2018
ER -