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Title: Towards locally differentially private generic graph metric estimation
Authors: Ye, Q 
Hu, H 
Au, MH 
Meng, X
Xiao, X
Issue Date: 2020
Source: Proceedings - International Conference on Data Engineering, 20-24 April 2020, Dallas, TX, USA, p. 1922-1925
Abstract: 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.
Keywords: Graph metric
Local differential privacy
Privacy-preserving graph analysis
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-7281-2903-7 (Electronic)
978-1-7281-2904-4 (Print on Demand(PoD))
DOI: 10.1109/ICDE48307.2020.00204
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication Q. Ye, H. Hu, M. H. Au, X. Meng and X. Xiao, "Towards Locally Differentially Private Generic Graph Metric Estimation," 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA, 2020, pp. 1922-1925 is available at
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