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Article: LF-GDPR: A Framework for Estimating Graph Metrics with Local Differential Privacy
Title | LF-GDPR: A Framework for Estimating Graph Metrics with Local Differential Privacy |
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Authors | |
Keywords | Local differential privacy Graph metric Privacy-preserving graph analysis |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers . The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=69 |
Citation | IEEE Transactions on Knowledge and Data Engineering, 2020, Epub 2020-12-24 How to Cite? |
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. To address low data utility of LDP, it optimally allocates privacy budget between the two atomic metrics during data collection. To demonstrate the usage of LF-GDPR, we show use cases on two common graph analysis tasks, namely, clustering coefficient estimation and community detection. The privacy and utility achieved by LF-GDPR are verified through theoretical analysis and extensive experimental results. |
Persistent Identifier | http://hdl.handle.net/10722/305587 |
ISSN | 2023 Impact Factor: 8.9 2023 SCImago Journal Rankings: 2.867 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ye, Q | - |
dc.contributor.author | Hu, H | - |
dc.contributor.author | Au, MH | - |
dc.contributor.author | Meng, X | - |
dc.contributor.author | Xiao, X | - |
dc.date.accessioned | 2021-10-20T10:11:31Z | - |
dc.date.available | 2021-10-20T10:11:31Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Knowledge and Data Engineering, 2020, Epub 2020-12-24 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | http://hdl.handle.net/10722/305587 | - |
dc.description.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. To address low data utility of LDP, it optimally allocates privacy budget between the two atomic metrics during data collection. To demonstrate the usage of LF-GDPR, we show use cases on two common graph analysis tasks, namely, clustering coefficient estimation and community detection. The privacy and utility achieved by LF-GDPR are verified through theoretical analysis and extensive experimental results. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers . The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=69 | - |
dc.relation.ispartof | IEEE Transactions on Knowledge and Data Engineering | - |
dc.rights | IEEE Transactions on Knowledge and Data Engineering. Copyright © Institute of Electrical and Electronics Engineers . | - |
dc.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. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Local differential privacy | - |
dc.subject | Graph metric | - |
dc.subject | Privacy-preserving graph analysis | - |
dc.title | LF-GDPR: A Framework for Estimating Graph Metrics with Local Differential Privacy | - |
dc.type | Article | - |
dc.identifier.email | Au, MH: manhoau@hku.hk | - |
dc.identifier.authority | Au, MH=rp02638 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/TKDE.2020.3047124 | - |
dc.identifier.scopus | eid_2-s2.0-85098783403 | - |
dc.identifier.hkuros | 327803 | - |
dc.identifier.volume | Epub 2020-12-24 | - |
dc.identifier.spage | 8 | - |
dc.identifier.epage | 8 | - |
dc.identifier.isi | WOS:000853844700024 | - |
dc.publisher.place | United States | - |