323 research outputs found
Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities
Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy
Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges
The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy
Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest
achievements in AI development. The content generated by related applications,
such as text, images and audio, has sparked a heated discussion. Various
derived AIGC applications are also gradually entering all walks of life,
bringing unimaginable impact to people's daily lives. However, the rapid
development of such generative tools has also raised concerns about privacy and
security issues, and even copyright issues in AIGC. We note that advanced
technologies such as blockchain and privacy computing can be combined with AIGC
tools, but no work has yet been done to investigate their relevance and
prospect in a systematic and detailed way. Therefore it is necessary to
investigate how they can be used to protect the privacy and security of data in
AIGC by fully exploring the aforementioned technologies. In this paper, we
first systematically review the concept, classification and underlying
technologies of AIGC. Then, we discuss the privacy and security challenges
faced by AIGC from multiple perspectives and purposefully list the
countermeasures that currently exist. We hope our survey will help researchers
and industry to build a more secure and robust AIGC system.Comment: 43 pages, 10 figure
cRVR: A Stackelberg Game Approach for Joint Privacy-Aware Video Requesting and Edge Caching
As users conveniently stream their favored online videos, video request
records will be automatically seized by video content providers, which may leak
users' privacy. Unfortunately, most existing privacy-enhancing approaches are
not applicable for protecting users' privacy in requests, which cannot be
easily altered or distorted by users and must be visible for content providers
to stream correct videos. To preserve request privacy in online video services,
it is possible to request additional videos irrelevant to users' interests so
that content providers cannot precisely infer users' interest information.
However, a naive redundant requesting approach will significantly degrade the
performance of edge caches and increase bandwidth overhead accordingly. In this
paper, we are among the first to propose a Cache-Friendly Redundant Video
Requesting (cRVR) algorithm for User Devices (UDs) and its corresponding
caching algorithm for the Edge Cache (EC), which can effectively mitigate the
problem of request privacy leakage with minimal impact on the EC's performance.
To solve the problem, we develop a Stackelberg game to analyze the dedicated
interaction between UDs and EC and obtain their optimal strategies to maximize
their respective utility. For UDs, the utility function is a combination of
both video playback utility and privacy protection utility. We theoretically
prove the existence and uniqueness of the equilibrium of the Stackelberg game.
In the end, extensive experiments are conducted with real traces to demonstrate
that cRVR can effectively protect video request privacy by reducing up to
57.96\% of privacy disclosure compared to baseline algorithms. Meanwhile, the
caching performance of ECs is only slightly affected
Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning
A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency
Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC
Cyber Security
This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security
Security and Privacy on Generative Data in AIGC: A Survey
The advent of artificial intelligence-generated content (AIGC) represents a
pivotal moment in the evolution of information technology. With AIGC, it can be
effortless to generate high-quality data that is challenging for the public to
distinguish. Nevertheless, the proliferation of generative data across
cyberspace brings security and privacy issues, including privacy leakages of
individuals and media forgery for fraudulent purposes. Consequently, both
academia and industry begin to emphasize the trustworthiness of generative
data, successively providing a series of countermeasures for security and
privacy. In this survey, we systematically review the security and privacy on
generative data in AIGC, particularly for the first time analyzing them from
the perspective of information security properties. Specifically, we reveal the
successful experiences of state-of-the-art countermeasures in terms of the
foundational properties of privacy, controllability, authenticity, and
compliance, respectively. Finally, we summarize the open challenges and
potential exploration directions from each of theses properties
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
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