SPG: Structure-Private Graph Database via SqueezePIR
SPG: Structure-Private Graph Database via SqueezePIR
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DOI：
10.14778/3587136.3587138
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Ling Liang;Jilan Lin;Zheng Qu;Ishtiyaque Ahmad;Fengbin Tu;Trinabh Gupta;Yufei Ding;Yuan Xie
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文献类型：
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作者：
Ling Liang;Jilan Lin;Zheng Qu;Ishtiyaque Ahmad;Fengbin Tu;Trinabh Gupta;Yufei Ding;Yuan Xie
我们日常生活中的很多关系数据都是用图来表示的，这使得图应用成为一个重要的工作量。由于图数据集的规模很大，将图数据移动到云端成为一种流行的选择。为了使机密和私有图免受不受信任的云服务器的攻击，许多加密技术被用来隐藏数据的内容。然而，仅仅保护数据内容对于图数据库是不够的。因为通过数据库访问轨迹可以揭示图的结构信息。在这项工作中，我们研究了图神经网络（GNN），一个重要的图形工作量，从图形数据库中挖掘信息。我们发现，服务器能够推断出哪个节点在边缘检索阶段正在处理，并在GNN的聚合阶段学习其邻居索引。这导致了图结构数据的信息泄漏。在这项工作中，我们提出了SPG，一个结构私有图数据库与SqueezePIR。我们的SPG是建立在私有信息检索（PIR）之上的，它可以安全地隐藏访问的节点/邻居。此外，我们提出了SqueezePIR，一种压缩技术，以克服PIR的计算开销。根据我们的评估，与最先进的FastPIR协议相比，我们的SqueezePIR平均实现了11.85倍的加速比，准确率损失不到2%。
Many relational data in our daily life are represented as graphs, making graph application an important workload. Because of the large scale of graph datasets, moving graph data to the cloud becomes a popular option. To keep the confidential and private graph secure from an untrusted cloud server, many cryptographic techniques are leveraged to hide the content of the data. However, protecting only the data content is not enough for a graph database. Because the structural information of the graph can be revealed through the database accessing track. In this work, we study the graph neural network (GNN), an important graph workload to mine information from a graph database. We find that the server is able to infer which node is processing during the edge retrieving phase and also learn its neighbor indices during GNN's aggregation phase. This leads to the leakage of the information of graph structure data. In this work, we present SPG, a structure-private graph database with SqueezePIR. Our SPG is built on top of Private Information Retrieval (PIR), which securely hides which nodes/neighbors are accessed. In addition, we propose SqueezePIR, a compression technique to overcome the computation overhead of PIR. Based on our evaluation, our SqueezePIR achieves 11.85× speedup on average with less than 2% accuracy loss when compared to the state-of-the-art FastPIR protocol.