TY - JOUR
T1 - HERMES
T2 - A Privacy-Preserving Approximate Search Framework for Big Data
AU - Zhou, Zhigang
AU - Zhang, Hongli
AU - Li, Shang
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/12/28
Y1 - 2018/12/28
N2 - We propose a sampling-based framework for privacy-preserving approximate data search in the context of big data. The framework is designed to bridge multi-target query needs from users and the data platform, including required query accuracy, timeliness, and query privacy constraints. A novel privacy metric, (ϵ,δ)-approximation, is presented to uniformly measure accuracy, efficiency and privacy breach risk. Based on this, we employ bootstrapping to efficiently produce approximate results that meet the preset query requirements. Moreover, we propose a quick response mechanism to deal with homogeneous queries, and discuss the reusage of results when appending data. Theoretical analyses and experimental results demonstrate that the framework is capable of effectively fulfilling multi-target query requirements with high efficiency and accuracy.
AB - We propose a sampling-based framework for privacy-preserving approximate data search in the context of big data. The framework is designed to bridge multi-target query needs from users and the data platform, including required query accuracy, timeliness, and query privacy constraints. A novel privacy metric, (ϵ,δ)-approximation, is presented to uniformly measure accuracy, efficiency and privacy breach risk. Based on this, we employ bootstrapping to efficiently produce approximate results that meet the preset query requirements. Moreover, we propose a quick response mechanism to deal with homogeneous queries, and discuss the reusage of results when appending data. Theoretical analyses and experimental results demonstrate that the framework is capable of effectively fulfilling multi-target query requirements with high efficiency and accuracy.
KW - Big data
KW - Hadoop
KW - bootstrapping
KW - metrics
KW - privacy-preserving
KW - sampling
UR - http://www.scopus.com/inward/record.url?scp=85040049525&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040049525&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2017.2788013
DO - 10.1109/ACCESS.2017.2788013
M3 - Article
AN - SCOPUS:85040049525
VL - 6
SP - 20009
EP - 20020
JO - IEEE Access
JF - IEEE Access
ER -