BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence

This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.

/pdf/security-and-privacy-for-green-iot-based-agriculture-review-3ibflqt0zu.pdf

Security and Privacy for Green IoT-Based Agriculture: Review, Blockchain Solutions, and Challenges

Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance is ineffective to some extent. A privacy leakage, including personal information, location, media data, etc., during the transmission process from IoT devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in EC enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy tradeoffs is designed to balance the privacy preservation and execution performance. Eventually, the experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.

Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT

Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

Security and privacy for 6G: A survey on prospective technologies and challenges

When the Internet and other interconnected networks are used in a health system, it is referred to as “e-Health.” In this paper, we examined research studies from 2017–2020 to explore the utilization of intelligent techniques in health and its evolution over time, particularly the integration of Internet of Things (IoT) devices and cloud computing. E-Health is defined as “the ability to seek, find, understand and appraise health information derived from electronic sources and acquired knowledge to properly solve or treat health problems. As a repository for health information as well as e-Health analysis, the Internet has the potential to protect consumers from harm and empower them to participate fully in informed health-related decision-making. Most importantly, high levels of e-Health integration mitigate the risk of encountering unreliable information on the Internet. Various research perspectives related to security and privacy within IoT-cloud-based e-Health systems are examined, with an emphasis on the opportunities, benefits and challenges of the implementation such systems. The combination of IoT-based e-Health systems integrated with intelligent systems such as cloud computing that provide smart objectives and applications is a promising future trend.

Security and Privacy in IoT-Cloud-Based e-Health Systems—A Comprehensive Review

Internet of Things (IoT) devices have brought much efficiency and convenience to our daily life. However, the devices may collect a myriad of data from people without their consent. Controlling the large amount of data generated from the devices from being misused is critical to mitigate privacy risks. Therefore, privacy protection on personal data has become an important factor in the development of the IoT. Historically, privacy enhancing technologies (PETs) can effectively enhance the privacy and protect users’ personally identifiable information. To date, many researchers have stressed the importance of PETs and proposed solutions relevant to different application fields of the IoT. However, to the best of our knowledge, none of the research has analyzed the PETs in IoT from the aspects of privacy threat issues and privacy legislation. As a result, this paper surveys on the solutions of PETs in the field of IoT, which has filtered down from the large number of published academic papers to the 120 primary studies published between 2014 and 2017. After collecting the papers, we categorized them based on the functions and the coverage of privacy protection, and analyzed them from different aspects, ranging from high-level principles of general data protection regulations and ISO/IEC 29100:2011 requirements to the actual resolution of privacy threats in IoT. Thus, we aim to identify the current state of development of the PETs in various fields and examine whether the existing PETs comply with the latest legal principles and privacy standards and reduce the threats to privacy. Finally, recommendations for future research are given based on the results.

Tzu-Yang Hsu

Papers

Privacy Enhancing Technologies in the Internet of Things: Perspectives and Challenges