Accelerating Graph Embedding Through Secure Distributed Outsourcing Computation in Internet of Things

Pengyu Cui, Yunting Tao, Bin Zhen, Fanyu Kong, Chunpeng Ge, Chuan Ma, Jia Yu

Abstract: With the advancement of the Internet of Things (IoT), numerous machine learning applications on IoT are encountering performance bottlenecks. Graph embedding is an emerging type of machine learning that has achieved commendable results in areas, such as network anomaly detection, malware detection, IoT device management, and service recommendation within the IoT. However, for some resource-constrained IoT devices, computing graph embedding algorithms is highly complex and time consuming. In this article, we introduce an efficient and secure distributed outsourcing scheme, employing four noncolluding cloud servers to facilitate the computation of graph embedding for the IoT devices. Our scheme utilizes a novel blinding factor generated through the QR decomposition to blind matrices containing sensitive information. We partition the blinded matrix into several segments, distributing different small matrix blocks across four servers, each of which executes only a portion of the computational tasks. The proposed outsourcing solution ensures the privacy of input and output information is not compromised. In our scheme, we utilize an effective verification method that can detect the erroneous behaviors of cloud servers with a probability close to one. Theoretical analysis and experimental results indicate that our solution achieves a computational efficiency of $(35 m^{2}+2 m)/(3 m^{3})$ compared to the original algorithm.