TY - JOUR
T1 - Privacy preserving distributed data mining based on secure multi-party computation
AU - Liu, Jun
AU - Tian, Yuan
AU - Zhou, Yu
AU - Xiao, Yang
AU - Ansari, Nirwan
N1 - Funding Information:
This work is supported by the project of User Privacy Protection of Internet of Vehicle Data , No. 18DZ2202500 , Science and Technology Commission Shanghai Municipality, China .
Publisher Copyright:
© 2020
PY - 2020/3/1
Y1 - 2020/3/1
N2 - Data mining is an important task to understand the valuable information for making correct decisions. Technologies for mining self-owned data of a party are rather mature. However, how to perform distributed data mining to obtain information from data owned by multiple parties without privacy leakage remains a big challenge. While secure multi-party computation (MPC) may potentially address this challenge, several issues have to be overcome for practical realizations. In this paper, we point out two unsupported tasks of MPC that are common in the real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results show that our design and implementation are feasible and effective for privacy preserving distributed data mining.
AB - Data mining is an important task to understand the valuable information for making correct decisions. Technologies for mining self-owned data of a party are rather mature. However, how to perform distributed data mining to obtain information from data owned by multiple parties without privacy leakage remains a big challenge. While secure multi-party computation (MPC) may potentially address this challenge, several issues have to be overcome for practical realizations. In this paper, we point out two unsupported tasks of MPC that are common in the real-world. Towards this end, we design algorithms based on optimized matrix computation with one-hot encoding and LU decomposition to support these requirements in the MPC context. In addition, we implement them based on a SPDZ protocol, a computation framework of MPC. The experimental evaluation results show that our design and implementation are feasible and effective for privacy preserving distributed data mining.
KW - Matrix optimization
KW - Multi-party computation
KW - Privacy preserving computing
KW - SPDZ protocol
KW - Secret sharing
KW - Secure data mining
UR - http://www.scopus.com/inward/record.url?scp=85079093057&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079093057&partnerID=8YFLogxK
U2 - 10.1016/j.comcom.2020.02.014
DO - 10.1016/j.comcom.2020.02.014
M3 - Article
AN - SCOPUS:85079093057
SN - 0140-3664
VL - 153
SP - 208
EP - 216
JO - Computer Communications
JF - Computer Communications
ER -