The Library
Two-mode networks : inference with as many parameters as actors and differential privacy
Tools
Wang, Qiuping, Yan, Ting, Jiang, Banyan and Leng, Chenlei (2022) Two-mode networks : inference with as many parameters as actors and differential privacy. Journal of Machine Learning Research, 23 (292). pp. 1-38. ISSN 1532-4435.
|
PDF
WRAP-two-mode-networks-inference-many-parameters-actors-differential-privacy-Leng-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (755Kb) | Preview |
|
PDF
WRAP-two-mode-networks-inference-many-parameters-actors-differential-privacy-Leng-2022.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (727Kb) |
Official URL: https://jmlr.org/papers/v23/20-1255.html
Abstract
Many network data encountered are two-mode networks. These networks are characterized by having two sets of nodes and links are only made between nodes belonging to different sets. While their two-mode feature triggers interesting interactions, it also increases the risk of privacy exposure, and it is essential to protect sensitive information from being disclosed when releasing these data. In this paper, we introduce a weak notion of edge differential privacy and propose to release the degree sequence of a two-mode network by adding non-negative Laplacian noises that satisfies this privacy definition. Under mild conditions for an exponential-family model for bipartite graphs in which each node is individually parameterized, we establish the consistency and Asymptotic normality of two differential privacy estimators, the first based on moment equations and the second after denoising the noisy sequence. For the latter, we develop an efficient algorithm which produces a readily useful synthetic bipartite graph. Numerical simulations and a real data application are carried out to verify our theoretical results and demonstrate the usefulness of our proposal.
Item Type: | Journal Article | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Alternative Title: | ||||||||||||||||
Subjects: | Q Science > QA Mathematics Q Science > QC Physics |
|||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Mathematical statistics -- Asymptotic theory, Statistical mechanics, Artificial intelligence, Computer security | |||||||||||||||
Journal or Publication Title: | Journal of Machine Learning Research | |||||||||||||||
Publisher: | M I T Press | |||||||||||||||
ISSN: | 1532-4435 | |||||||||||||||
Official Date: | 2022 | |||||||||||||||
Dates: |
|
|||||||||||||||
Volume: | 23 | |||||||||||||||
Number: | 292 | |||||||||||||||
Page Range: | pp. 1-38 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 21 November 2022 | |||||||||||||||
Date of first compliant Open Access: | 9 December 2022 | |||||||||||||||
RIOXX Funder/Project Grant: |
|
|||||||||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year