# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "GRANDpriv" in publications use:' type: software license: GPL-3.0-or-later title: 'GRANDpriv: Graph Release with Assured Node Differential Privacy' version: 0.1.3 doi: 10.32614/CRAN.package.GRANDpriv abstract: Implements a novel method for privatizing network data using differential privacy. Provides functions for generating synthetic networks based on LSM (Latent Space Model), applying differential privacy to network latent positions to achieve overall network privatization, and evaluating the utility of privatized networks through various network statistics. The privatize and evaluate functions support both LSM and RDPG (Random Dot Product Graph). For generating RDPG networks, users are encouraged to use the 'randnet' package . For more details, see the "proposed method" section of Liu, Bi, and Li (2025) . authors: - family-names: Liu given-names: Suqing email: liusuqing0123@uchicago.edu - family-names: Bi given-names: Xuan email: xbi@umn.edu - family-names: Li given-names: Tianxi email: tianxili@umn.edu repository: https://lsq0000.r-universe.dev repository-code: https://github.com/lsq0000/GRANDpriv commit: 0daafe90007ecf698a94256f3416b721ee0a97c4 url: https://github.com/lsq0000/GRANDpriv date-released: '2025-08-23' contact: - family-names: Liu given-names: Suqing email: liusuqing0123@uchicago.edu