TY - GEN
T1 - Towards Locally Differentially Private Generic Graph Metric Estimation
AU - Ye, Qingqing
AU - Hu, Haibo
AU - Au, Man Ho Allen
AU - Meng, Xiaofeng
AU - Xiao, Xiaokui
PY - 2020/4/20
Y1 - 2020/4/20
N2 - Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics — the adjacency bit vector and node degree — from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step.
AB - Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics — the adjacency bit vector and node degree — from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step.
KW - Graph metric
KW - Local differential privacy
KW - Privacy-preserving graph analysis
UR - https://www.utdallas.edu/icde/
UR - http://www.scopus.com/inward/record.url?scp=85085867468&partnerID=8YFLogxK
U2 - 10.1109/ICDE48307.2020.00204
DO - 10.1109/ICDE48307.2020.00204
M3 - Conference article published in proceeding or book
T3 - Proceedings - International Conference on Data Engineering
SP - 1922
EP - 1925
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
Y2 - 20 April 2020 through 24 April 2020
ER -