A Security and Privacy-Preserving Approach Based on Data Disturbance for Collaborative Edge Computing in Social IoT Systems
ABSTARCT :
The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people's lives in all directions. The rapid development of the IoT technology accelerates the process of the era of ``Internet of everything'' but also changes the role of terminal equipment at the edge of the network. It has changed from a single data user to a dual role of both producing and using data. And collaborative edge computing (CEC) has been born in time. CEC itself can not only solve the problem of computing and storage but also combines with the deep learning (DL) model to make full use of edge computing ability. However, as the core of DL, the robustness of neural network is often not high. In addition, edge devices of CEC are facing a highly dynamic environment, which can easily cause the edge network to be attacked by malicious devices. Therefore, user privacy protection and security issues for CEC deserve more attention. To avoid privacy leakage and security crisis of CEC in social IoT systems, a data protection method based on data disturbance method and adversarial training viewpoint is introduced in this article. Besides, a new adversarial sample generation method based on the firefly algorithm (FA) is proposed. This method reduces the time complexity of traditional by an order for magnitude compared with traditional generative adversarial network (GAN) generation. Since sentences, information on CEC in the IoT system is characterized by a large amount of data, strict confidentiality, and high-security requirements, and they are usually high-risk information on privacy leakage. The proposed method is conducted to the sentence similarity analysis model based on a convolutional neural network (CNN) in the CEC scene to test the feasibility of the method. Compared with the original CNN, the accuracy of the model using the confrontation training method is improved by 4.8%. At the same time, the security value...
EXISTING SYSTEM :
? The raw sensor signal’s data processing is essential, and a variety of existing solutions are addressed in this paper.
? The incremental model discussed in is the model that updates the parameters of the existing model depending on the previous incoming data, rather than constructing a new model from scratch.
? The benefit of applying semantic technology to sensor data is the conceptualization and abstract interpretation of the raw data, making them computer-definable, and interlinking the data with existing data web resources.
? This also allows access to domain information and related semantically enriched representations for other entities and/or existing data (on the web).
DISADVANTAGE :
? The development of security solutions for EC distributed heterogeneous architectures is a challenging problem.
? we combine the current cloud computing infrastructure with the edge computing (EC) paradigm to efficiently address the aforementioned problems.
? With different transmission protocols, comes the problem of interconnectivity, which is caused mainly by the heterogeneity of communication protocols used by edge devices.
? The problem of developing a mechanism to provide edge users with easy, safe, and secure access to distributed data storage and, at the same time, maintaining edge user privacy is still an open research direction.
PROPOSED SYSTEM :
• The proposed technique involves the reconstruction of subspace-based data sampling.
• The authors proposed a multiple segmented imputation approach, in which the data gap was identified and segmented into pieces, and then imputed and reconstructed iteratively with relevant data.
• Multiple feature extraction techniques and various classification algorithms were considered, as were the proposed processing depth and amplification of gain through efficient methods.
• The squared prediction error score was proposed in order to identify the data outliers.
• The proposed data analytics framework exhibited efficient data aggregation and data outlier detection with high accuracy.
ADVANTAGE :
? The performance of EC and cloud computing architectures are also compared in some IoT applications, such as smart transportation, smart city, and smart grid.
? The authors in study the security, privacy, and some efficiency challenges of data processing in mobile EC.
? To prevent intruders from accessing the network, a sophisticated but efficient authentication mechanism is required.
? Therefore, developing an authentication mechanism that utilizes the available resources efficiently is an issue.
? In, the authors propose an efficient Edge-Fog authentication scheme, to securely allow mutual authentication between Fog user and any Fog server.
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