发表机构
Zhejiang University; Hong Kong Baptist University（浙江大学; 香港浸会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对不可信收集者场景下图数据隐私泄露风险，提出LDPGraph算法，利用邻域结构在本地差分隐私下合成图，通过聚合扰动邻接列表估计三角形计数并联合修正度数，采用三角形优先策略重建图，实验验证其优越性。
AI 中文摘要
图数据的广泛应用不可避免地带来显著的隐私风险，因为未受保护的使用可能导致敏感信息泄露。在不可信收集者（untrusted curator）的场景中，这些风险尤为严重，此时数据保持去中心化，每个用户仅持有与其邻居的连接。为缓解此类隐私风险，我们采用本地差分隐私（LDP）来收集用户的私有信息并生成合成图。然而，现有方法要么因扰动本地邻接列表而注入过多噪声，要么因过于简单的图编码过程而导致显著的结构信息损失。为解决这些问题，我们提出LDPGraph，一种有效的图合成算法，该算法更进一步，在LDP下利用邻域结构。为获取超出度数的邻域统计信息，LDPGraph从扰动后的邻接列表中聚合出带噪声的全局视图，并将其与投影的本地连接相结合，以估计节点级三角形计数。为纠正因分别扰动度数和三角形计数而导致的结构不一致性，LDPGraph将二者联合修正为可行的度数-三角形目标。为从这些估计目标重建全局图，LDPGraph采用三角形优先策略，先保留局部聚类结构，再满足剩余度数要求。在四个真实数据集和多种常用图指标上的大量实验验证了LDPGraph的优越性。源代码可在该https URL获取。
英文摘要
The widespread application of graph data inevitably brings significant privacy risks, as its unprotected use can lead to the leakage of sensitive information. These risks are particularly acute in the setting with an untrusted curator, where the data remains decentralized and each user only holds the connections to their neighbors. To mitigate such privacy risks, we adopt local differential privacy (LDP) to collect users' private information and generate a synthetic graph. However, existing methods suffer from either excessive noise injection by perturbing the local adjacency lists or significant structural information loss due to the simplistic graph encoding process. To address these issues, we propose LDPGraph, an effective graph synthesis algorithm that takes one step further by exploiting neighborhood structures under LDP. To obtain neighborhood statistics beyond degrees, LDPGraph aggregates a noisy global view from perturbed adjacency lists and combines it with projected local connections to estimate node-level triangle counts. To correct the structural inconsistency caused by separately perturbing degree and triangle count, LDPGraph jointly refines them into feasible degree-triangle targets. To reconstruct a global graph from these estimated targets, LDPGraph adopts a triangle-first strategy that first preserves local clustering structures and then fulfills remaining degree requirements. Extensive experiments on four real-world datasets and multiple commonly used graph metrics validate the superiority of LDPGraph. Source code is available at https://github.com/ZJU-TrustAID/LDPGraph.
CommentsAccepted at IEEE ICDE 2027. Extended version with appendices
