Security Considerations for Virtual Reality Systems

Viswanathan, Karthik; Yazdinejad, Abbas

doi:10.48550/arxiv.2201.02563

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…The experiments demonstrate the accuracy and ability of semantic web technologies to eliminate noisy hyperlinks. In the preceding mechanisms as well as others such as [1], [20], [21], [22], [23], [24], existing data collections are not used for information retrieval; rather, noisy hyperlinks are removed through analysis of the web structure graph. Therefore, we need to construct a data collection of hyperlinks.…”

Section: Related Workmentioning

confidence: 99%

Implementation of a noisy hyperlink removal system: A semantic and relatedness approach

Taghandiki¹,

Ehsan²

2023

Preprint

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As the volume of data on the web grows, the web structure graph, which is a graph representation of the web, continues to evolve. The structure of this graph has gradually shifted from content-based to non-content-based. Furthermore, spam data, such as noisy hyperlinks, in the web structure graph adversely affect the speed and efficiency of information retrieval and link mining algorithms. Previous works in this area have focused on removing noisy hyperlinks using structural and string approaches. However, these approaches may incorrectly remove useful links or be unable to detect noisy hyperlinks in certain circumstances. In this paper, a data collection of hyperlinks is initially constructed using an interactive crawler. The semantic and relatedness structure of the hyperlinks is then studied through semantic web approaches and tools such as the DBpedia ontology. Finally, the removal process of noisy hyperlinks is carried out using a reasoner on the DBpedia ontology. Our experiments demonstrate the accuracy and ability of semantic web technologies to remove noisy hyperlinks.

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Section: Related Workmentioning

confidence: 99%

Implementation of a noisy hyperlink removal system: A semantic and relatedness approach

Taghandiki¹,

Ehsan²

2023

Preprint

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“…Our central objective is to develop an effective and auditable privacy-preserving model for federated and blockchain-based settings, addressing the research challenges outlined. Additionally, we intend to apply and generalize our approaches for auditable and privacy-preserving threat detection techniques in these environments [47], [48]. The subsequent subsection discusses the contributions in greater detail.…”

Section: Networkmentioning

confidence: 99%

“…Therefore, it is essential to explore privacy-preserving and auditable solutions. Conducting data protection audits can help determine compliance with GDPR, the Data Protection Act (DPA) 2018, and identify any areas requiring improvement [48], [62].…”

Section: Significance Of the Studymentioning

confidence: 99%

Efficient Privacy-Preserving Framework for Federated and Blockchain Networks

Khordadpour¹

2023

Preprint

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<p>Machine learning (ML) models, at the core of Artificial intelligence (AI), are widely applied in our digital world. To build these models, huge amounts of data must be collected, and many assets must be protected under privacy law. Data privacy is a critical issue when training and testing ML models. For privacy concerns to be adequately addressed in today’s ML systems, there needs to be considered privacy gaps in ML, as trained ML models can be vulnerable to adversarial attacks. In this regard, federated learning and blockchain networks are the new paradigms that have emerged with the promise of privacy-preserving by design while utilizing ML models. The new paradigms have promising privacy-preserving potential; however, they neglect several fundamental privacy and security issues the fact that adversaries can exploit shared gradients and global parameters, and the parameter server may drop gradients that have been mistakenly or deliberately updated. Also, enough data is available to train models. The proposed research addresses privacy concerns in federated and distributed environments. This is a general overview to establish a privacy-preserving framework that can provide privacy in federated and blockchain-based networks while address to fundamental privacy and security issues.</p>

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“…However, the lack of audit mechanisms can open the door to potential misuse and violations. Hence, our study focuses on a framework that combines federated and distributed environments to preserve privacy and enable auditability in AI and ML fields [51,52].…”

Section: Significance Of the Studymentioning

confidence: 99%

IoV-SFL: A Blockchain-based Federated Learning Framework for Secure and Efficient Data Sharing in the Internet of Vehicles

Ullah,

Deng,

Pei

et al. 2023

Preprint

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The emergence of the Internet of Vehicles (IoV) signifies a transformative paradigm for intelligent and interconnected transportation systems. However, the effective sharing of data within the IoV ecosystem presents significant challenges including security, privacy, and efficiency. To address these challenges, this paper introduces the IoV-SFL, a novel framework that seamlessly integrates Federated Learning (FL) and blockchain technology to provide a comprehensive solution. By employing federated learning, IoV-SFL framework ensures that data remains on local devices, preserving user privacy, while consortium blockchain enhances security and transparency. Furthermore, the IoV-SFL framework also leverages the capabilities of Homomorphic Encryption (HE) and Dynamic Scaling Factor to enable privacy-preserving computations, optimize resource allocation, and enhance efficiency. The incorporation of Gated Recurrent Units (GRU) within the Convolutional Neural Networks (CNN) model empowers the IoV-SFL framework to extract meaningful insights from heterogeneous data streams. Rigorous experiments have validated the superior performance of the IoV-SFL framework compared to conventional approaches in terms of security, communication efficiency, model accuracy, and convergence speed. Additionally, a comprehensive security and privacy analysis highlights IoV-SFL's robustness against diverse threats and vulnerabilities. The IoV-SFL framework represents a groundbreaking advancement, offering a secure and efficient means of data sharing in the Internet of Vehicles (IoV), paving the way for innovative vehicular applications and safer transportation systems.

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Security Considerations for Virtual Reality Systems

Cited by 6 publications

References 23 publications

Implementation of a noisy hyperlink removal system: A semantic and relatedness approach

Implementation of a noisy hyperlink removal system: A semantic and relatedness approach

Efficient Privacy-Preserving Framework for Federated and Blockchain Networks

IoV-SFL: A Blockchain-based Federated Learning Framework for Secure and Efficient Data Sharing in the Internet of Vehicles

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