LDP-Based Social Content Protection for Trending Topic Recommendation

Wei, Jianhao; Li, Junyi; Yao, Lin; Zhang, Jin

doi:10.1109/jiot.2020.3026366

“…The storage of these private and sensitive information (e.g., user profiling) of massive users in cloud servers or edge devices may also raise privacy disclosure issues. For example, hackers may deduce users' privacy information by frequent queries via differential attacks [40] and even compromise the cloud/edge storage via DDoS attacks [43].…”

Section: Privacy Threatsmentioning

confidence: 99%

A Survey on Metaverse: Fundamentals, Security, and Privacy

wang¹,

Zhou²,

Zhang³

et al. 2022

Preprint

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<div>Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialize. Driven by recent advances in emerging technologies such as extended reality, artificial intelligence, and blockchain, metaverse is stepping from the science fiction to an upcoming reality. However, severe privacy invasions and security breaches (inherited from underlying technologies or emerged in the new digital ecology) of metaverse can impede its wide deployment. At the same time, a series of fundamental challenges (e.g., scalability and interoperability) can arise in metaverse security provisioning owing to the intrinsic characteristics of metaverse, such as immersive realism, hyper spatiotemporality, sustainability, and heterogeneity.</div><div>In this paper, we present a comprehensive survey of the fundamentals, security, and privacy of metaverse.</div><div>Specifically, we first investigate a novel distributed metaverse architecture and its key characteristics with ternary-world interactions. Then, we discuss the security and privacy threats, present the critical challenges of metaverse systems, and review the state-of-the-art countermeasures. Finally, we draw open research directions for building future metaverse systems.</div>

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“…In metaverse systems, massive private and sensitive user data collected from various XR devices (e.g., helmets) are transferred via wired and wireless communications, the confidentiality of which should be prohibited from unauthorized individuals/services. Although communications are encrypted and information is confidentially transmitted, adversaries may still access the raw data by eavesdropping on the specific channel and even track users' locations via differential attacks [40] and advanced inference attacks [41].…”

Section: Privacy Threatsmentioning

confidence: 99%

“…3) Privacy-Preserving UGC Sharing and Processing: Existing privacy-preserving schemes for data sharing and processing mainly focus on four fields: differential privacy (DP), federated learning (FL), cryptographic approaches (e.g., secure multiparty computation (SMC), homomorphic encryption (HE), and zero-knowledge proof (ZKP)), and trusted computing. The following works [40], [91]- [94] discuss privacy-preserving UGC sharing in the metaverse. To offer privacy-preserving trending topic recommendation services in the metaverse, Wei et al [40] propose a graph-based local DP mechanism, where a compressive sensing indistinguishability method is devised to produce noisy social topics to prevent user-linkage association and protect keyword correlation privacy with high efficiency.…”

Section: B Data Managementmentioning

confidence: 99%

“…The following works [40], [91]- [94] discuss privacy-preserving UGC sharing in the metaverse. To offer privacy-preserving trending topic recommendation services in the metaverse, Wei et al [40] propose a graph-based local DP mechanism, where a compressive sensing indistinguishability method is devised to produce noisy social topics to prevent user-linkage association and protect keyword correlation privacy with high efficiency. To enable smart health sensing without violating users' private data in the metaverse, Zhang et al [91] present a FL-based secure data collaboration framework where wearable sensors periodically send local model updates trained on their private sensory data to the server which synthesizes a global abnormal health detection model.…”

Section: B Data Managementmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Metaverse: Fundamentals, Security, and Privacy

wang¹,

Zhou²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

<div>Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialize. Driven by recent advances in emerging technologies such as extended reality, artificial intelligence, and blockchain, metaverse is stepping from the science fiction to an upcoming reality. However, severe privacy invasions and security breaches (inherited from underlying technologies or emerged in the new digital ecology) of metaverse can impede its wide deployment. At the same time, a series of fundamental challenges (e.g., scalability and interoperability) can arise in metaverse security provisioning owing to the intrinsic characteristics of metaverse, such as immersive realism, hyper spatiotemporality, sustainability, and heterogeneity.</div><div>In this paper, we present a comprehensive survey of the fundamentals, security, and privacy of metaverse.</div><div>Specifically, we first investigate a novel distributed metaverse architecture and its key characteristics with ternary-world interactions. Then, we discuss the security and privacy threats, present the critical challenges of metaverse systems, and review the state-of-the-art countermeasures. Finally, we draw open research directions for building future metaverse systems.</div>

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“…Table II lists some of the common computing tasks in SIoT and the literature of social-aware solutions. Trust management in SIoT [44], [45], [46], [47], [48], [49], [16], [50] Establishing trust between SIoT devices for social clustering and social community detection Social features extractions and classification [39], [38], [51], [52] Assigning social properties such as personality traits, mood, emotions and interest to SIoT users Social relationships management in SIoT [53], [54], [55], [56], [57], [58], [59] Maintaining the user-user, user-device and devicedevice social relationship history and logging the relationship properties and preferences Social-aware recommendation system in SIoT [43], [60], [61], [62], [63], [64] Services recommendation and content customization based on the social properties of SIoT entities Security and privacy in SIoT [65], [66], [67], [68], [69] Securing and preserving the privacy of social data and social context…”

Section: A Asi In Computingmentioning

confidence: 99%

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

Dhelim

¹

,

Ning

²

,

Farha

³

et al. 2021

IEEE Internet Things J.

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With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deeplearning techniques.

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LDP-Based Social Content Protection for Trending Topic Recommendation

Cited by 26 publications

References 41 publications

A Survey on Metaverse: Fundamentals, Security, and Privacy

A Survey on Metaverse: Fundamentals, Security, and Privacy

A Survey on Metaverse: Fundamentals, Security, and Privacy

IoT-Enabled Social Relationships Meet Artificial Social Intelligence

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