TY - GEN
T1 - A novel privacy-preserving distributed anomaly detection method
AU - Zhang, Chunkai
AU - Liu, Haodong
AU - Li, Ye
AU - Yin, Ao
AU - Jiang, Zoe L.
AU - Liao, Qing
AU - Wang, Xuan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Anomaly detection refers to the algorithm to find the anomalies among the data. As a branch of data mining, it has important research significance. With the advance of sensor technology, data is always distributed at many places. To ensure that the data owners privacy data is not disclosed in the process of anomaly detection, the privacy preserving scheme is necessary. In this paper, we propose a provable secure structure, Secure Isolation Forest(SIF), which is a distributed anomaly detection algorithm based on ensemble isolation principle. We improve performance and detection capabilities by fixed the height of trees and adopt an effective homomorphic cryptosystem. Our construction allows the inputs encrypted by different independent public keys. Lastly, we highlight the practicability of our construction by extensive experimental evaluation.
AB - Anomaly detection refers to the algorithm to find the anomalies among the data. As a branch of data mining, it has important research significance. With the advance of sensor technology, data is always distributed at many places. To ensure that the data owners privacy data is not disclosed in the process of anomaly detection, the privacy preserving scheme is necessary. In this paper, we propose a provable secure structure, Secure Isolation Forest(SIF), which is a distributed anomaly detection algorithm based on ensemble isolation principle. We improve performance and detection capabilities by fixed the height of trees and adopt an effective homomorphic cryptosystem. Our construction allows the inputs encrypted by different independent public keys. Lastly, we highlight the practicability of our construction by extensive experimental evaluation.
KW - Distributed Anomaly Detection Semi-Honest model
KW - Secure Isolation Forest
UR - https://www.scopus.com/pages/publications/85050611270
U2 - 10.1109/SPAC.2017.8304323
DO - 10.1109/SPAC.2017.8304323
M3 - 会议稿件
AN - SCOPUS:85050611270
T3 - 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
SP - 463
EP - 468
BT - 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2017
Y2 - 15 December 2017 through 17 December 2017
ER -