Building In-the-Cloud Network Functions: Security and Privacy Challenges

Jiang, Peipei; Wang, Qian; Huang, Muqi; Wang, Cong; Li, Qi; Shen, Chao; Ren, Kui

doi:10.1109/jproc.2021.3127277

Cited by 16 publications

(3 citation statements)

References 165 publications

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“…According to a case study released by the Cloud Security Alliance (CSA), the top three threats in cloud computing are data breach, insufficient identity, credential and access management, and insecure interfaces and Application Program Interfaces (APIs) [54]. Cloud virtualization security [55,56], cloud data security [57][58][59], and cloud application security [60,61] are also widely discussed. Other recognized issues related to security and privacy in cloud computing include multi-tenancy, confidentiality, and phishing [62].…”

Section: Security Collaborationmentioning

confidence: 99%

A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities

Yang

Lee

et al. 2022

Sensors

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Information and Communication Technology (ICT) makes cities “smart”, capable of providing advanced municipal services to citizens more efficiently. In the literature, many applications of municipal service platform based on cloud computing and edge computing have been proposed, but the reference model and application instance based on cloud-edge collaboration specially for municipal service platform is rarely studied. In this context, this paper first develops a reference model, including resource collaboration, application collaboration, service collaboration, and security collaboration, and discusses the main contents and challenges of each part. Then, aiming at the problem of computing and communication resources allocation in the cloud-edge collaboration, a game-theory-based dynamic resource allocation model is introduced. Finally, an e-government self-service system based on the cloud-edge collaboration is designed and implemented. The cloud side is a cloud computing server, and the edge side are the self-service terminals integrating various edge computing devices with Artificial Intelligence (AI) embedded. The experimental results show that the designed system combines the advantages of cloud computing and edge computing, and provides a better user experience with lower processing latency, larger bandwidth, and more concurrent tasks. Meanwhile, the findings show that the evolutionary equilibrium and the Nash equilibrium are the optimal solutions, respectively.

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Section: Security Collaborationmentioning

confidence: 99%

A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities

Yang

Lee

et al. 2022

Sensors

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“…However, skyline query services, if deployed in the cloud, may raise critical privacy concerns regarding the outsourced databases and skyline queries, which may contain proprietary/privacy-sensitive information. Moreover, the cloud might even suffer from data breaches [4], [5] which would seriously harm data privacy. These critical concerns necessitate Yifeng Zheng, Weibo Wang, Songlei Wang, and Zhongyun Hua are with the School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China (e-mail: yifeng.zheng@hit.edu.cn, weibo.wang.hitsz@outlook.com, songlei.wang@outlook.com, huazhongyun@hit.edu.cn).…”

Section: Introductionmentioning

confidence: 99%

Towards Efficient and Privacy-Preserving User-Defined Skyline Query Over Single Cloud

Zhang

Ray

et al. 2023

IEEE Trans. Dependable and Secure Comput.

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The proliferation of cloud computing has greatly spurred the popularity of outsourced database storage and management, in which the cloud holding outsourced databases can process database queries on demand. Among others, skyline queries play an important role in the database field due to its prominent usefulness in multi-criteria decision support systems. To accommodate the tailored needs of users, userdefined skyline query has recently emerged as an intriguing advanced type of skyline query, which allows users to define custom preferences in their skyline queries (including the target attributes, preferred dominance relations, and range constraints on the target attributes). However, user-defined skyline query services, if deployed in the cloud, may raise critical privacy concerns as the outsourced databases and skyline queries may contain proprietary/privacy-sensitive information, and the cloud might even suffer from data breaches. In light of the above, this paper presents ObliuSky, a new system framework enabling oblivious user-defined skyline query processing in the cloud. ObliuSky departs from the state-of-the-art prior work by not only providing confidentiality protection for the content of the outsourced database, the user-defined skyline query, and the query results, but also making the cloud oblivious to the data patterns (e.g., user-defined dominance relations among database points and search access patterns) which may indirectly cause data leakages. We formally analyze the security guarantees and conduct extensive performance evaluations. The results show that while achieving much stronger security guarantees than the stateof-the-art prior work, ObliuSky is superior in database and query encryption efficiency, with practically affordable query latency.

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“…GNNs can empower a variety of graph-centric applications such as node classification [6], edge classification [7] and link prediction [8]. With the widespread adoption of cloud computing, it is increasingly popular to deploy machine learning training and inference services in the cloud [9], [10], due to the well-understood benefits [11], [12]. However, GNN training and inference, if deployed in the public cloud, will raise critical severe privacy concerns.…”

Section: Introductionmentioning

confidence: 99%

SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service

Wang¹,

Zheng²,

Jia³

2022

Preprint

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Graphs are widely used to model the complex relationships among entities. As a powerful tool for graph analytics, graph neural networks (GNNs) have recently gained wide attention due to its end-to-end processing capabilities. With the proliferation of cloud computing, it is increasingly popular to deploy the services of complex and resource-intensive model training and inference in the cloud due to its prominent benefits. However, GNN training and inference services, if deployed in the cloud, will raise critical privacy concerns about the information-rich and proprietary graph data (and the resulting model). While there has been some work on secure neural network training and inference, they all focus on convolutional neural networks handling images and text rather than complex graph data with rich structural information. In this paper, we design, implement, and evaluate SecGNN, the first system supporting privacy-preserving GNN training and inference services in the cloud. SecGNN is built from a synergy of insights on lightweight cryptography and machine learning techniques. We deeply examine the procedure of GNN training and inference, and devise a series of corresponding secure customized protocols to support the holistic computation. Extensive experiments demonstrate that SecGNN achieves comparable plaintext training and inference accuracy, with practically affordable performance.

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Building In-the-Cloud Network Functions: Security and Privacy Challenges

Abstract: The article surveys the state-of-the-art literature on network function outsourcing, with a special focus on privacy and security issues.

Cited by 16 publications

References 165 publications

A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities

A Design and Application of Municipal Service Platform Based on Cloud-Edge Collaboration for Smart Cities

Towards Efficient and Privacy-Preserving User-Defined Skyline Query Over Single Cloud

SecGNN: Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service

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