A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System

Sun, Jianfei; Xu, Guowen; Zhang, Tianwei; Alazab, Mamoun; Deng, Robert H.

doi:10.1109/tifs.2022.3196271

Cited by 28 publications

(6 citation statements)

References 41 publications

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“…The puncturable encryption (PE) approach was included in the attribute‐based matchmaking encryption (AB‐ME) method to create the novel authentication approach named fine‐grained puncturable matchmaking encryption (FP‐ME) was described by Sun et al 63 Implementing the introduced model assures the validity of the clients' requests, and fulfilling their deadlines was devised instantly using the encryption technique. Delivering a user request within the specified deadline in the authenticated format through the newly designed encryption criteria assures the privacy preservation of a fog computing environment.…”

Section: Techniques To Overcome the Security Issues Of Fogmentioning

confidence: 99%

“…Intrusion detection systems : Various attacks in the fog layer are man‐in‐the‐middle attacks, insider attacks, dos attacks, scanning attacks and so on, in which secure information sharing by mitigating the attack is a challenging task. However, security in all the three‐tier architecture is essential for safer information sharing 60–69 …”

Section: Challenges Identifiedmentioning

confidence: 99%

“…However, security in all the three-tier architecture is essential for safer information sharing. [60][61][62][63][64][65][66][67][68][69] Distributed architecture: The decentralized nature of fog devices makes security issues more challenging because many edge devices utilize the same code for information sharing. Thus, the redundancy of a system model must be minimized to enhance the security of a fog environment 42,45,[47][48][49]51 Computation hierarchy: The cloud servers are located far from the fog, which generates a trade-off between computation power and response time in information sharing.…”

Section: Challenges Identifiedmentioning

confidence: 99%

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Security analysis of fog computing environment for ensuring the security and privacy of information

Gola,

Arya,

Khan

et al. 2023

Trans Emerging Tel Tech

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In fog computing, the management, measurement, configuration, control, communication, and storage tasks are carried out collaboratively by one or more near‐user or end users. Fog computing is widely utilized in major application domains like centralized fog utilized in a shopping complex (UXFog), agriculture domain (FoAgro), health‐care domain (MediFog), vehicle parking system, and energy Lattices. The execution of fog computing at the system's edge provides the elevated quality of service (QoS), promotes area mindfulness, and minimal dormancy for the continuous and online streaming application areas. Fog networking is extremely concerned about wireless system security due to the prevalence of wireless communication in fog computing. Sniffer attacks, Jamming assaults, and several other intrusions are considered examples of security issues. While considering network storage issues, factors like interaction management, dynamic workload, and low dormancy are the most challenging aspects of designing the network model. Thus, privacy and security issues are crucial while designing the network model, accomplished through intrusion detection, access control and authentication methods. Therefore, the security analysis of fog computing based on authentication, access control and intrusion detection mechanisms are analyzed in this research to design a novel technique with enhanced security and privacy features for sharing and accessing information.

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Section: Techniques To Overcome the Security Issues Of Fogmentioning

confidence: 99%

Section: Challenges Identifiedmentioning

confidence: 99%

Section: Challenges Identifiedmentioning

confidence: 99%

See 1 more Smart Citation

Security analysis of fog computing environment for ensuring the security and privacy of information

Gola,

Arya,

Khan

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…What is more, users can update their keys themselves without the need for the trusted center to reissue a new key. There are some recent works related to PE still emerging [27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Fine-Grained Forward Secure Firmware Update in Smart Home

et al. 2023

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Although the vigorous development of smart homes brings great convenience to people’s lives, smart homes usually suffer from various security threats due to firmware vulnerabilities. Firmware update is a possible solution, but existing methods cannot address the security issues during the update process well. To enable secure firmware updates, a Forward Secure Firmware Update (FSFU) system was realized based on the proposed Puncturable-Ciphertext Policy-Attribute-Based Encryption (P-CP-ABE) scheme. In FSFU, when the service provider delivers the latest firmware, it specifies an access policy and some tags to encrypt the data and appends its signature to achieve both fine-grained access control and authentication. Authorized customers can obtain the latest firmware by decrypting the encrypted data through their private key. In particular, after a successful update, each authorized customer can realize forward security by updating his/her puncturable key, which is an important private key component. In addition, FSFU is further enhanced by outsourcing a part of the parameters and computational tasks. Finally, FSFU was proven to be secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Our proposed FSFU is efficient from both the theoretical analysis and the experimental results.

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“…With the deep integration and application of artificial intelligence in different industries [ 1 , 2 , 3 ] and social life [ 4 , 5 , 6 ], model training needs more participants. In this paper, we discuss how to motivate participants to provide their multi−dimensional information, such as data [ 7 , 8 ] and computing power.…”

Section: Introductionmentioning

confidence: 99%

SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions

Chen

Wang

et al. 2022

Sensors

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As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high−quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi−dimensional auctions. Therefore, we (1) propose a multi−dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi−dimensional auction has practical significance and can be used in verification experiments.

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A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System

Cited by 28 publications

References 41 publications

Security analysis of fog computing environment for ensuring the security and privacy of information

Security analysis of fog computing environment for ensuring the security and privacy of information

Fine-Grained Forward Secure Firmware Update in Smart Home

SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions

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