TFL-DT: A Trust Evaluation Scheme for Federated Learning in Digital Twin for Mobile Networks

Guo, Jingjing; Liu, Zhiquan; Tian, Siyi; Huang, Feiran; Li, Jiaxing; Li, Xinghua; Igorevich, Kostromitin Konstantin; Ma, Jianfeng

doi:10.1109/jsac.2023.3310094

Cited by 78 publications

(17 citation statements)

References 26 publications

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“…This scheme addresses limitations found in existing methods, particularly the reliance on single assessment factors and coarse-grained trust computation methods. Through comprehensive experiments, Guo demonstrates the effectiveness and superiority of the proposed scheme [17]. Meanwhile, taxi trajectory data are widely used in other applications.…”

Section: Introductionmentioning

confidence: 85%

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

Chang,

Chen,

et al. 2024

Sustainability

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With the advancement of urban modernization, more and more residents are flocking to large cities, leading to problems such as severe traffic congestion, uneven distribution of spatial resources, and deterioration of the urban environment. These challenges pose a serious threat to the coordinated development of cities. In order to better understand the travel behavior of metropolitan residents and provide valuable insights for urban planning, this study utilizes taxi trajectory data from the central areas of Beijing, Shanghai, Shenzhen, and Wuhan. First, the relationship between daytime taxi drop-off points and urban amenities is explored using Ordinary Least Squares (OLS). Subsequently, Geographically Weighted Regression (GWR) techniques were applied to identify spatial differences in these urban drivers. The results show that commonalities emerge across the four cities in the interaction between external transport stops and commercial areas. In addition, the average daily travel patterns of residents in these four cities show a trend of “three peaks and three valleys”, indicating the commonality of travel behavior. In summary, this study explores the travel characteristics of urban residents, which can help urban planners understand travel patterns more effectively. This is crucial for the strategic allocation of transport resources across regions, the promotion of sustainable urban transport, and the reduction in carbon emissions.

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Section: Introductionmentioning

confidence: 85%

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

Chang,

Chen,

et al. 2024

Sustainability

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“…The scheme proposed in Ref. [ 37 ] takes various behavior attributes of participants for trust calculation. The system accurately computes the trustworthiness of participants with diverse behavior patterns.…”

Section: Related Workmentioning

confidence: 99%

SGO: Semantic Group Obfuscation for Location-Based Services in VANETS

Ullah,

Shah

2024

Sensors

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Location privacy is an important parameter to be addressed in the case of vehicular ad hoc networks. Each vehicle frequently communicates with location-based services to find the nearest location of interest. The location messages communicated with the location server may contain sensitive information like vehicle identity, location, direction, and other headings. A Location-Based Services (LBS) server is not a trusted entity; it can interact with an adversary, compromising the location information of vehicles on the road and providing a way for an adversary to extract the future location tracks of a target vehicle. The existing works consider two or three neighboring vehicles as a virtual shadow to conceal location information. However, they did not fully utilize the semantic location information and pseudonym-changing process, which reduces the privacy protection level. Moreover, a lot of dummy location messages are generated that increase overheads in the network. To address these issues, we propose a Semantic Group Obfuscation (SGO) technique that utilizes both location semantics as well as an efficient pseudonym-changing scheme. SGO creates groups of similar status vehicles on the road and selects random position coordinates for communication with the LBS server. It hides the actual location of a target vehicle in a vicinity. The simulation results verify that the proposed scheme SGO improves the anonymization and entropy of vehicles, and it reduces the location traceability and overheads in the network in terms of computation cost and communication cost. The cost of overhead is reduced by 55% to 65% compared with existing schemes. We also formally model and specify SGO using High-Level Petri Nets (HLPNs), which show the correctness and appropriateness of the scheme.

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“…In [30], the authors propose a trust evaluation scheme for federated learning in DTMN, which takes direct trust evidence and recommended trust information into account. In [31], a novel trust cascading-based emergency message dissemination (TCEMD) model is proposed, which incorporates the entity-oriented trust values into data-oriented trust evaluation in an efficient manner.…”

Section: Related Workmentioning

confidence: 99%

Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things

Messina,

Rosaci,

Sarnè

2024

Electronics

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Key aspects of communities of the Internet of Things (IoT) smart objects presenting social aspects are represented by trust and reputation relationships between the objects. Several trustworthiness models have been presented in the literature in the context of multi-smart object community that could be adopted in the IoT scenario; however, most of these approaches represent the different dimensions of trust using scalar measures, then integrating these measures in a global trustworthiness value. In this paper, we discuss the limitation of this approach in the IoT context, highlighting the necessity of modeling complex trust relationships that cannot be captured by a vector-based model, and we propose a new trust model in which the trust perceived by an object with respect to another object is modeled by a directed, weighted graph whose vertices are trust dimensions and whose arcs represent relationships between trust dimensions. By using this new model, we provide the IoT community with the possibility of representing also situations in which an object does not know a trust dimension, e.g., reliability, but it is able to derive it from another one, e.g., honesty. The introduced model can represent any trust structure of the type illustrated above, in which several trust dimensions are mutually dependent.

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TFL-DT: A Trust Evaluation Scheme for Federated Learning in Digital Twin for Mobile Networks

Cited by 78 publications

References 26 publications

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

SGO: Semantic Group Obfuscation for Location-Based Services in VANETS

Applying Trust Patterns to Model Complex Trustworthiness in the Internet of Things

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