Open AccessPublishing Social Graphs with Differential Privacy Guarantees Based on wPINQ
Author(s) -
Li Xiaoye,
Yang Jing,
Sun Zhenlong,
Zhang Jianpei
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.12.003
Subject(s) - differential privacy , computer science , publishing , differential (mechanical device) , internet privacy , data publishing , computer security , data mining , political science , engineering , law , aerospace engineering
To publish social graphs with differential privacy guarantees for reproducing valuable results of scientific researches, we study a workflow for graph synthesis and propose an improved approach based on weighted Privacy integrated query (wPINQ). The workflow starts with a seed graph to fit the noisy degree sequence, which essentially is the 1K‐graph. In view of the inaccurate assortativity coefficient, we truncate the workflow to replace the seed graph with an optimal one by doing target 1K‐rewiring while preserving the 1K‐distribution. Subsequently, Markov chain Monte Carlo employs the new seed graph as the initial state, and proceeds step by step guided by the information of Triangles by intersect to increase the number of triangles in the synthetic graphs. The experimental results show that the proposed algorithm achieves better performance for the published social graphs.