Recurrent neural network (RNN), a branch of deep learning, is a powerful model for sequential data that has outstanding performance on a wide range of important Internet of Things (IoT) tasks. This unprecedented growth of RNN model has however encountered both heterogeneous IoT data and privacy issues. Existing RNN model can not deal with heterogeneous sequential data; often the larger datasets used in training of RNN model contain sensitive information. To tackle these challenges and for the first time, this research proposes a novel differentially private tensor-based RNN (DPTRNN) that can be applied in many challenging deep learning sequence tasks for IoT systems. Specifically, to process heterogeneous sequential data, we propose a tensor-based RNN model. To guarantee privacy, we develop a tensor-based back-propagation through time algorithm with perturbation to avoid exposing the sensitive information for training the tensor-based RNN model within the framework of differential privacy. Thorough security analysis shows that the differential private tensor-based RNN efficiently protects the confidentiality of sensitive user information for IoT. Our results from extensive experiments on two challenging large video datasets suggest that our proposed scheme is practical with guarantee of data privacy preservation and acceptable accuracy loss.

Secure two-party neural network (2P-NN) inference allows the server with a neural network model and the client with inputs to perform neural network inference without revealing their private data to each other. However, the state-of-the-art 2P-NN inference still suffers from large computation and communication overhead especially when used in ImageNet-scale deep neural networks. In this work, we design and build Panther, a lightweight and efficient secure 2P-NN inference system, which has great efficiency in evaluating 2P-NN inference while safeguarding the privacy of the server and the client. At the core of Panther, we have new protocols for 2P-NN inference. Firstly, we propose a customized homomorphic encryption scheme to reduce burdensome polynomial multiplications in the homomorphic encryption arithmetic circuit of linear protocols. Secondly, we present a more efficient and communication concise design for the millionaires’ protocol, which enables non-linear protocols with less communication cost. Our evaluations over three sought-after varying-scale deep neural networks show that Panther outperforms the state-of-the-art 2P-NN inference systems in terms of end-to-end runtime and communication overhead. Panther achieves state-of-the-art performance with up to 24.95× speedup for linear protocols and 6.40× speedup for non-linear protocols in WAN when compared to prior arts.

With the advent of the 5G era, the Internet of Things (IoT) will flourish in the future. Millions of IoT devices will be connected by 5G networks, which will bring great challenges to network management. Software defined network (SDN) is a novel solution for managing a large number of IoT devices over the network. In order to solve the problems of secure data analysis in SDN-based IoT, a differentially private tensor computing model (DPTCM) is proposed in this paper. Our approach utilizes tensor to model and analyze the SDN-based IoT big data, and an algorithm named differentially private tensor train decomposition (DPTTD) is proposed to achieve secure computing in SDN-based IoT. The algorithm can make full use of the flexible computing power of edge-cloud computing so as to realize collaborative computing between edges, cloud, and the third-party. By separating calculation process of the private data and non-private data, the algorithm implements the localized calculation and preservation of the original data which can protect the data security from the source. Meanwhile, we use differential privacy technology to protect the privacy of data transmitted to the cloud. Finally, we prove the algorithm satisfies ε-differential privacy. In the experiments, we verify our model on two real-world datasets, the experimental results show that differential privacy has a little side effect on prediction results, our model has good performance in data prediction.

There are three key roles in Intelligent Transportation Systems (ITS): driver, vehicle and road. However, existing static interactions among Driver-Vehicle-Road (DVR) are too passive to reflect the change of driver preferences, vehicle conditions, road conditions, etc. Therefore, we provide a data-driven Cloud-Fog-Edge Collaborative Driver-Vehicle-Road (CFEC-DVR) framework. The framework could self-adaptively evolves through continuous iteration to provide better ITS services for humans. The collaboration among DVR creates a lot of relation data that construct our relation graphs. Cloud brings some privacy risks. Relation graphs have great analytic value. As DVR collaboration, privacy quality and analytic accuracy are three key issues in the framework, we propose a Relation Graph Privacy-Preserving scheme with High Accuracy in our framework, which is named as RGPP-HA. Based on machine learning, our method nearly maximizes the difficulty for attackers to know exactly how many other roles are connected to the attacked role, which enhances the privacy quality. Meanwhile, we find as much valuable information as possible from roles’ encrypted relations for more accurately analytic performance. Based on the experiments, we compare the proposed scheme RGPP-HA with existing classic and relevant schemes. The experimental results show that our scheme has the best privacy quality and analytic accuracy. This further verifies the feasibility of CFEC-DVR framework.

With the ever-increasing network traffic and Internet connectivity of smart devices, more attack events are being reported. As a result, network forensics remains a topic of ongoing research interest in the Internet of Things (IoT). In this article, we present a novel tensor-based forensics approach for virtualized network functions (VNFs). An event tensor model is proposed to formalize the network events, and then, it is used for effectively updating the core event tensor. We then introduce a similarity tensor model to integrate the core event tensors on the orchestration and management layer in the network function virtualization (NFV) framework. Finally, we present an evidence tensor model for network forensics, where we demonstrate how evidence tensors can be merged.