Understanding Security in Smart City Domains From the ANT-Centric Perspective

Fan, Jiani; Yang, Wenzhuo; Liu, Ziyao; Kang, Jiawen; Niyato, Dusit; Lam, Kwok-Yan; Du, Hongyang

doi:10.1109/jiot.2023.3252040

Cited by 13 publications

(6 citation statements)

References 120 publications

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“…We show the full protocol for privacy-enhanced neural network using our optimized FHE scheme in Algorithm 13 3 .…”

Section: G Our Frameworkmentioning

confidence: 99%

“…Besides, AIaaS plays an important role in the development of smart cities by supporting efficient implementation of intelligent distributed solutions, which typically also involve the adoption of Internet of Things (IoT) technology to gather data from the physical environment for automated control and decision-making at the cloud. However, the data handling process may lead to privacy and security concerns [1]- [3]. When K.Y.…”

Section: Introductionmentioning

confidence: 99%

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Efficient FHE-based Privacy-Enhanced Neural Network for Trustworthy AI-as-a-Service

Lam,

Lu,

Zhang

et al. 2024

IEEE Trans. Dependable and Secure Comput.

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AI-as-a-Service has emerged as an important trend for supporting the growth of the digital economy. Digital service providers make use of their vast amount of customer data to train AI models (such as image recognition, financial modelling and pandemic modelling etc) and offer them as a service on the cloud. While there are convincing advantages for using such third-party models, the fact that model users are required to upload their data to the cloud is bound to raise serious privacy concerns, especially in the face of increasingly stringent privacy regulations and legislation.To promote the adoption of AI-as-a-Service while addressing privacy issues, we propose a practical approach for constructing privacy-enhanced neural networks by designing an efficient implementation of fully homomorphic encryption. With this approach, an existing neural network can be converted to process FHE-encrypted data and produce encrypted output which are only accessible by the model users, and more importantly, within an operationally acceptable time (e.g. within 1 second for facial recognition in typical border control systems). Experimental results show that in many practical tasks such as facial recognition, text classification and so on, we obtained the state-of-the-art inference accuracy in less than one second on a 16 cores CPU.

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“…We show the full protocol for privacy-enhanced neural network using our optimized FHE scheme in Algorithm 13 3 .…”

Section: G Our Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient FHE-based Privacy-Enhanced Neural Network for Trustworthy AI-as-a-Service

Lam,

Lu,

Zhang

et al. 2024

IEEE Trans. Dependable and Secure Comput.

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show abstract

“…It is also in charge of providing its subsidiary networks with critical traffic information, such as wide-area networks with traffic signaling and roadside infrastructure. Commonly, centrally or remotely located public and third-party servers are used to store the data created here (2). Due to the high mobility of IoVs, roadside units (4) and nearby vehicles (3) frequently serve as data relays to promote effective traffic information exchange and provide a smooth connection to IoVs.…”

Section: A Our Proposalmentioning

confidence: 99%

Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks

Jiani

Shar

Guo

et al. 2022

Network and System Security

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The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this paper, by modeling the data sharing ecosystem as a data trading market, we propose a decentralized data-sharing incentive mechanism based on multiintelligent reinforcement learning to learn the supply-demand balance in markets and minimize transmission latency. Our proposed mechanism takes into account the dynamic nature of IoV markets, which can experience frequent fluctuations in supply and demand. We propose a time-sensitive Key-Policy Attribute-Based Encryption (KP-ABE) mechanism coupled with Named Data Networking (NDN) to protect data in IoVs, which adds a layer of security to our proposed solution. Additionally, we design a decentralized market for efficient data sharing in IoVs, where continuous double auctions are adopted. The proposed mechanism based on multi-agent deep reinforcement learning can learn the supply-demand equilibrium in markets, thus improving the efficiency and sustainability of markets. Theoretical analysis and experimental results show that our proposed learning-based incentive mechanism outperforms baselines by 10% in determining the equilibrium of supply and demand while reducing transmission latency by 20%.

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“…Fan et al [2] adopt an ANT-centric perspective to understand security in the realm of Smart Cities and the Internet of Things (IoT). Their study provides valuable insights into the complex interplay of security factors within smart urban environments.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

Jabakumar,

Dhablia

2022

RJCSE

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This work delves into the pressing need for more efficient Predictive Maintenance solutions in the context of IoT-enabled Smart Cities. Existing methodologies often fall short, lacking the precision and accuracy required to keep these rapidly evolving urban environments running smoothly for different use cases. The limitations of current approaches become apparent when considering their inability to cope with the intricacies of IoT data. They struggle to harness the wealth of information generated by countless interconnected devices and systems, resulting in suboptimal performance. This issue is further compounded by their relatively sluggish response times, hindering the timely detection of critical maintenance needs. In response to these challenges, this paper presents an innovative approach that leverages the fusion of Bidirectional Long Short-Term Memory (BiLSTM) with Autoencoders in conjunction with Recurrent Neural Networks (RNNs). This combination of advanced techniques brings forth a powerful predictive maintenance model process. The utilization of BiLSTM and Autoencoders adds a layer of sophistication, allowing for a deeper understanding of the underlying data patterns. BiLSTM's ability to capture contextual information from both past and future data points enriches the model's predictive capabilities. The inclusion of Autoencoders aids in feature extraction and reconstruction, enhancing the model's ability to discern relevant information sets. The advantages of this proposed model are profound. It exhibits 4.9% higher precision, ensuring that maintenance actions are precisely targeted, thus reducing unnecessary interventions. Moreover, the model achieves a remarkable 5.5% increase in accuracy, guaranteeing more reliable predictions. Its 4.5% boost in recall ensures that potential issues are identified promptly. The model's 3.9% increase in speed means that maintenance responses are faster and more effective, minimizing downtemporal instance sets. Lastly, an 8.5% improvement in the Area Under the Curve (AUC) underscores its superior performance compared to existing methodologies. The impacts of this work extend far beyond the realm of predictive maintenance levels. It contributes significantly to the realization of truly smart and efficient cities, where resources are optimized, disruptions are minimized, and the quality of life for citizens is enhanced for different use cases. This research marks a pivotal step towards the seamless integration of IoT technologies into urban environments, with the potential to revolutionize the way we manage and maintain our cities in the future scenarios.

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Understanding Security in Smart City Domains From the ANT-Centric Perspective

Cited by 13 publications

References 120 publications

Efficient FHE-based Privacy-Enhanced Neural Network for Trustworthy AI-as-a-Service

Efficient FHE-based Privacy-Enhanced Neural Network for Trustworthy AI-as-a-Service

Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks

Predictive Maintenance for IoT-Enabled Smart Cities using Recurrent Neural Networks

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