With the popularization of AIoT applications, every endpoint device is facing information security risks. Thus, how to ensure the security of the device becomes essential. Chip security is divided into software security and hardware security, both of which are indispensable and complement each other. Hardware security underpins the entire cybersecurity ecosystem by proving essential primitives, including key provisioning, hardware cryptographic engines, hardware unique key (HUK), and unique identification (UID). This establishes a Hardware Root of Trust (HRoT) with secure storage, secure operation, and a secure environment to provide a trustworthy foundation for chip security. Today s talk starts with how to use a Physical Unclonable Function (PUF) to generate a unique “fingerprint” (static random number) for the chip. Next, we will address using a static random number and dynamic entropy to design a high-performance true random number generator and achieve real anti-tampering HRoT by leveraging static and dynamic entropy. By integrating NISTstandard cryptographic engines, we have created an authentic PUF-based Hardware Root of Trust. The all-in-one integrated solution can handle all the necessary security functions throughout the product life cycle as well as maintaining a secure boundary to achieve the integrity of sensitive information or assets. Finally, as hardware-level protection extends to operating systems and applications, products and services become secure.

This paper presents a case study about the initial phases of the interface design for an artificial intelligence-based decision-support system for clinical diagnosis. The study presents challenges and opportunities in implementing a human-centered design (HCD) approach during the early stages of the software development of a complex system. These methods are commonly adopted to ensure that the systems are designed based on users needs. For this project, they are also used to investigate the users potential trust issues and ensure the creation of a trustworthy platform. However, the project stage and heterogeneity of the teams can pose obstacles to their implementation. The results of the implementation of HCD methods have shown to be effective and informed the creation of low fidelity prototypes. The outcomes of this process can assist other designers, developers, and researchers in creating trustworthy AI solutions.

The Assessment List for Trustworthy AI (ALTAI) was developed by the High-Level Expert Group on Artificial Intelligence (AI HLEG) set up by the European Commission to help assess whether the AI system that is being developed, deployed, procured, or used, complies with the seven requirements of Trustworthy AI, as specified in the AI HLEG’s Ethics Guidelines for Trustworthy AI. This paper describes the self-evaluation process of the SHAPES pilot campaign and presents some individual case results applying the prototype of an interactive version of the Assessment List for Trustworthy AI. Finally, the available results of two individual cases are combined. The best results are obtained from the evaluation category ‘transparency’ and the worst from ‘technical robustness and safety’. Future work will be combining the missing self-assessment results and developing mitigation recommendations for AI-based risk reduction recommendations for new SHAPES services.

Recent advances in artificial intelligence, specifically machine learning, contributed positively to enhancing the autonomous systems industry, along with introducing social, technical, legal and ethical challenges to make them trustworthy. Although Trustworthy Autonomous Systems (TAS) is an established and growing research direction that has been discussed in multiple disciplines, e.g., Artificial Intelligence, Human-Computer Interaction, Law, and Psychology. The impact of TAS on education curricula and required skills for future TAS engineers has rarely been discussed in the literature. This study brings together the collective insights from a number of TAS leading experts to highlight significant challenges for curriculum design and potential TAS required skills posed by the rapid emergence of TAS. Our analysis is of interest not only to the TAS education community but also to other researchers, as it offers ways to guide future research toward operationalising TAS education.

The continuously growing importance of today’s technology paradigms such as the Internet of Things (IoT) and the new 5G/6G standard open up unique features and opportunities for smart systems and communication devices. Famous examples are edge computing and network slicing. Generational technology upgrades provide unprecedented data rates and processing power. At the same time, these new platforms must address the growing security and privacy requirements of future smart systems. This poses two main challenges concerning the digital processing hardware. First, we need to provide integrated trustworthiness covering hardware, runtime, and the operating system. Whereas integrated means that the hardware must be the basis to support secure runtime and operating system needs under very strict latency constraints. Second, applications of smart systems cover a wide range of requirements where "one- chip-fits-all" cannot be the cost and energy effective way forward. Therefore, we need to be able to provide a scalable hardware solution to cover differing needs in terms of processing resource requirements.In this paper, we discuss our research on an integrated design of a secure and scalable hardware platform including a runtime and an operating system. The architecture is built out of composable and preferably simple components that are isolated by default. This allows for the integration of third-party hardware/software without compromising the trusted computing base. The platform approach improves system security and provides a viable basis for trustworthy communication devices.

The traditional process of renting the house has several issues such as data security, immutability, less trust and high cost due to the involvement of third party, fraudulent agreement, payment delay and ambiguous contracts. To address these challenges, a blockchain with smart contracts can be an effective solution. This paper leverages the vital features of blockchain and smart contract for designing a trustworthy and secured house rental system. The proposed system involves offchain and on-chain transactions on hyperledger blockchain. Offchain transaction includes the rental contract creation between tenant and landlord based on their mutual agreement. On-chain transactions include the deposit and rent payment, digital key generation and contract dissolution, by considering the agreed terms and conditions in the contract. The functional and performance analysis of the proposed system is carried out by applying the different test cases. The proposed system fulfills the requirements of house rental process with high throughput (\textgreater92 tps) and affordable latency (\textless0.7 seconds).

With the development of networked embedded technology, the requirements of embedded systems are becoming more and more complex. This increases the difficulty of requirements analysis. Requirements patterns are a means for the comprehension and analysis of the requirements problem. In this paper, we propose seven functional requirements patterns for complex embedded systems on the basis of analyzing the characteristics of modern embedded systems. The main feature is explicitly distinguishing the controller, the system devices (controlled by the controller) and the external entities (monitored by the controller). In addition to the requirements problem description, we also provide observable system behavior description, I∼O logic and the execution mechanism for each pattern. Finally, we apply the patterns to a solar search subsystem of aerospace satellites, and all the 20 requirements can be matched against one of the patterns. This validates the usability of our patterns.

In order to assess the fire risk of the intelligent buildings, a trustworthy classification model was developed, which provides model supporting for the classification assessment of fire risk in intelligent buildings under the urban intelligent firefight construction. The model integrates Bayesian Network (BN) and software trustworthy computing theory and method, designs metric elements and attributes to assess fire risk from four dimensions of fire situation, building, environment and personnel; BN is used to calculate the risk value of fire attributes; Then, the fire risk attribute value is fused into the fire risk trustworthy value by using the trustworthy assessment model; This paper constructs a trustworthy classification model for intelligent building fire risk, and classifies the fire risk into five ranks according to the trustworthy value and attribute value. Taking the Shanghai Jing’an 11.15 fire as an example case, the result shows that the method provided in this paper can perform fire risk assessment and classification.

Fog computing moves computation from the cloud to edge devices to support IoT applications with faster response times and lower bandwidth utilization. IoT users and linked gadgets are at risk to security and privacy breaches because of the high volume of interactions that occur in IoT environments. These features make it very challenging to maintain and quickly share dynamic IoT data. In this method, cloud-fog offers dependable computing for data sharing in a constantly changing IoT system. The extended IoT cloud, which initially offers vertical and horizontal computing architectures, then combines IoT devices, edge, fog, and cloud into a layered infrastructure. The framework and supporting mechanisms are designed to handle trusted computing by utilising a vertical IoT cloud architecture to protect the IoT cloud after the issues have been taken into account. To protect data integrity and information flow for different computing models in the IoT cloud, an integrated data provenance and information management method is selected. The effectiveness of the dynamic scaling mechanism is then contrasted with that of static serving instances.

This paper first describes the security and privacy challenges for the Internet of Things IoT) systems and then discusses some of the solutions that have been proposed. It also describes aspects of Trustworthy Machine Learning (TML) and then discusses how TML may be applied to handle some of the security and privacy challenges for IoT systems.

Advances in the frontier of intelligence and system sciences have triggered the emergence of Autonomous AI (AAI) systems. AAI is cognitive intelligent systems that enable non-programmed and non-pretrained inferential intelligence for autonomous intelligence generation by machines. Basic research challenges to AAI are rooted in their transdisciplinary nature and trustworthiness among interactions of human and machine intelligence in a coherent framework. This work presents a theory and a methodology for AAI trustworthiness and its quantitative measurement in real-time context based on basic research in autonomous systems and symbiotic human-robot coordination. Experimental results have demonstrated the novelty of the methodology and effectiveness of real-time applications in hybrid intelligence systems involving humans, robots, and their interactions in distributed, adaptive, and cognitive AI systems.

Python continues to be one of the most popular programming languages and has been used in many safetycritical fields such as medical treatment, autonomous driving systems, and data science. These fields put forward higher security requirements to Python ecosystems. However, existing studies on machine learning systems in Python concentrate on data security, model security and model privacy, and just assume the underlying Python virtual machines (PVMs) are secure and trustworthy. Unfortunately, whether such an assumption really holds is still unknown.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

Blockchain technology promises to overcome trust and privacy concerns inherent to centralized information sharing. However, current decentralized supply chain management systems do either not meet privacy and scalability requirements or require a trustworthy consortium, which is challenging for increasingly dynamic supply chains with constantly changing participants. In this paper, we propose CCChain, a scalable and privacy-aware supply chain management system that stores all information locally to give companies complete sovereignty over who accesses their data. Still, tamper protection of all data through a permissionless blockchain enables on-demand tracking and tracing of products as well as reliable information sharing while affording the detection of data inconsistencies. Our evaluation confirms that CCChain offers superior scalability in comparison to alternatives while also enabling near real-time tracking and tracing for many, less complex products.

Blockchain has emerged as a leading technological innovation because of its indisputable safety and services in a distributed setup. Applications of blockchain are rising covering varied fields such as financial transactions, supply chains, maintenance of land records, etc. Supply chain management is a potential area that can immensely benefit from blockchain technology (BCT) along with smart contracts, making supply chain operations more reliable, safer, and trustworthy for all its stakeholders. However, there are numerous challenges such as scalability, coordination, and safety-related issues which are yet to be resolved. Multi-agent systems (MAS) offer a completely new dimension for scalability, cooperation, and coordination in distributed culture. MAS consists of a collection of automated agents who can perform a specific task intelligently in a distributed environment. In this work, an attempt has been made to develop a framework for implementing a multi-agent system for a large-scale product manufacturing supply chain with blockchain technology wherein the agents communicate with each other to monitor and organize supply chain operations. This framework eliminates many of the weaknesses of supply chain management systems. The overall goal is to enhance the performance of SCM in terms of transparency, traceability, trustworthiness, and resilience by using MAS and BCT.

In the context of cybersecurity systems, trust is the firm belief that a system will behave as expected. Trustworthiness is the proven property of a system that is worthy of trust. Therefore, trust is ephemeral, i.e. trust can be broken; trustworthiness is perpetual, i.e. trustworthiness is verified and cannot be broken. The gap between these two concepts is one which is, alarmingly, often overlooked. In fact, the pressure to meet with the pace of operations for mission critical cross domain solution (CDS) development has resulted in a status quo of high-risk, ad hoc solutions. Trustworthiness, proven through formal verification, should be an essential property in any hardware and/or software security system. We have shown, in "vCDS: A Virtualized Cross Domain Solution Architecture", that developing a formally verified CDS is possible. virtual CDS (vCDS) additionally comes with security guarantees, i.e. confidentiality, integrity, and availability, through the use of a formally verified trusted computing base (TCB). In order for a system, defined by an architecture description language (ADL), to be considered trustworthy, the implemented security configuration, i.e. access control and data protection models, must be verified correct. In this paper we present the first and only security auditing tool which seeks to verify the security configuration of a CDS architecture defined through ADL description. This tool is useful in mitigating the risk of existing solutions by ensuring proper security enforcement. Furthermore, when coupled with the agile nature of vCDS, this tool significantly increases the pace of system delivery.

This paper proposes a novel approach for privacy preserving face recognition aimed to formally define a trade-off optimization criterion between data privacy and algorithm accuracy. In our methodology, real world face images are anonymized with Gaussian blurring for privacy preservation. The anonymized images are processed for face detection, face alignment, face representation, and face verification. The proposed methodology has been validated with a set of experiments on a well known dataset and three face recognition classifiers. The results demonstrate the effectiveness of our approach to correctly verify face images with different levels of privacy and results accuracy, and to maximize privacy with the least negative impact on face detection and face verification accuracy.

Python continues to be one of the most popular programming languages and has been used in many safety-critical fields such as medical treatment, autonomous driving systems, and data science. These fields put forward higher security requirements to Python ecosystems. However, existing studies on machine learning systems in Python concentrate on data security, model security and model privacy, and just assume the underlying Python virtual machines (PVMs) are secure and trustworthy. Unfortunately, whether such an assumption really holds is still unknown.This paper presents, to the best of our knowledge, the first and most comprehensive empirical study on the security of CPython, the official and most deployed Python virtual machine. To this end, we first designed and implemented a software prototype dubbed PVMSCAN, then use it to scan the source code of the latest CPython (version 3.10) and other 10 versions (3.0 to 3.9), which consists of 3,838,606 lines of source code. Empirical results give relevant findings and insights towards the security of Python virtual machines, such as: 1) CPython virtual machines are still vulnerable, for example, PVMSCAN detected 239 vulnerabilities in version 3.10, including 55 null dereferences, 86 uninitialized variables and 98 dead stores; Python/C API-related vulnerabilities are very common and have become one of the most severe threats to the security of PVMs: for example, 70 Python/C API-related vulnerabilities are identified in CPython 3.10; 3) the overall quality of the code remained stable during the evolution of Python VMs with vulnerabilities per thousand line (VPTL) to be 0.50; and 4) automatic vulnerability rectification is effective: 166 out of 239 (69.46%) vulnerabilities can be rectified by a simple yet effective syntax-directed heuristics.We have reported our empirical results to the developers of CPython, and they have acknowledged us and already confirmed and fixed 2 bugs (as of this writing) while others are still being analyzed. This study not only demonstrates the effectiveness of our approach, but also highlights the need to improve the reliability of infrastructures like Python virtual machines by leveraging state-of-the-art security techniques and tools.

Active consumers have now been empowered thanks to the smart grid concept. To avoid fossil fuels, the demand side must provide flexibility through Demand Response events. However, selecting the proper participants for an event can be complex due to response uncertainty. The authors design a Contextual Consumer Rate to identify the trustworthy participants according to previous performances. In the present case study, the authors address the problem of new players with no information. In this way, two different methods were compared to predict their rate. Besides, the authors also refer to the consumer privacy testing of the dataset with and without information that could lead to the participant identification. The results found to prove that, for the proposed methodology, private information does not have a high impact to attribute a rate.

Security is a key concern across the world, and it has been a common thread for all critical sectors. Nowadays, it may be stated that security is a backbone that is absolutely necessary for personal safety. The most important requirements of security systems for individuals are protection against theft and trespassing. CCTV cameras are often employed for security purposes. The biggest disadvantage of CCTV cameras is their high cost and the need for a trustworthy individual to monitor them. As a result, a solution that is both easy and cost-effective, as well as secure has been devised. The smart door lock is built on Raspberry Pi technology, and it works by capturing a picture through the Pi Camera module, detecting a visitor's face, and then allowing them to enter. Local binary pattern approach is used for Face recognition. Remote picture viewing, notification, on mobile device are all possible with an IOT based application. The proposed system may be installed at front doors, lockers, offices, and other locations where security is required. The proposed system has an accuracy of 89%, with an average processing time is 20 seconds for the overall process.

Cloud computing is a model of service provisioning in heterogeneous distributed systems that encourages many researchers to explore its benefits and drawbacks in executing workflow applications. Recently, high-quality security protection has been a new challenge in workflow allocation. Different tasks may and may not have varied security demands, security overhead may vary for different virtual machines (VMs) at which the task is assigned. This paper proposes a Security Oriented Deadline-Aware workflow allocation (SODA) strategy in an IaaS cloud environment to minimize the risk probability of the workflow tasks while considering the deadline met in a deterministic environment. SODA picks out the task based on the highest security upward rank and assigns the selected task to the trustworthy VMs. SODA tries to simultaneously satisfy each task’s security demand and deadline at the maximum possible level. The simulation studies show that SODA outperforms the HEFT strategy on account of the risk probability of the cloud system on scientific workflow, namely CyberShake.

This paper presents a physically-secure wireless communication system utilizing orbital angular momentum (OAM) waves at 0.31THz. A trustworthy key distribution mechanism for symmetric key cryptography is proposed by exploiting random hopping among the orthogonal OAM-wave modes and phases. Keccak-f[400] based pseudorandom number generator provides randomness to phase distribution of OAM-wave modes for additional security. We assess the security vulnerabilities of using OAM modulation in a THz communication system under various physical-layer threat models as well as analyze the effectiveness of these threat models for varying attacker complexity levels under different conditions.

Ubiquitous environment embedded with artificial intelligent consist of heterogenous smart devices communicating each other in several context for the computation of requirements. In such environment the trust among the smart users have taken as the challenge to provide the secure environment during the communication in the ubiquitous region. To provide the secure trusted environment for the users of ubiquitous system proposed approach aims to extract behavior of smart invisible entities by retrieving their behavior of communication in the network and applying the recommendation-based filters using Deep learning (RBF-DL). The proposed model adopts deep learning-based classifier to classify the unfair recommendation with fair ones to have a trustworthy ubiquitous system. The capability of proposed model is analyzed and validated by considering different attacks and additional feature of instances in comparison with generic recommendation systems.

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present Rex, the first enclave-based decentralized CF recommender. Rex exploits Trusted execution environments (TEE), such as Intel software guard extensions (SGX), that provide shielded environments within the processor to improve convergence while preserving privacy. Firstly, Rex enables raw data sharing, which ultimately speeds up convergence and reduces the network load. Secondly, Rex fully preserves privacy. We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of Rex. Our experimental results demonstrate that through raw data sharing, Rex significantly decreases the training time by 18.3 x and the network load by 2 orders of magnitude over standard decentralized approaches that share only parameters, while fully protecting privacy by leveraging trustworthy hardware enclaves with very little overhead.

The healthcare industry is confronted with a slew of significant challenges, including stringent regulations, privacy concerns, and rapidly rising costs. Many leaders and healthcare professionals are looking to new technology and informatics to expand more intelligent forms of healthcare delivery. Numerous technologies have advanced during the last few decades. Over the past few decades, pharmacy has changed and grown, concentrating less on drugs and more on patients. Pharmaceutical services improve healthcare's affordability and security. The primary invention was a cyber-infrastructure made up of smart gadgets that are connected to and communicate with one another. These cyber infrastructures have a number of problems, including privacy, trust, and security. These gadgets create cyber-physical systems for pharmaceutical care services in p-health. In the present period, cyber-physical systems for pharmaceutical care services are dealing with a variety of important concerns and demanding conditions, i.e., problems and obstacles that need be overcome to create a trustworthy and effective medical system. This essay offers a thorough examination of CPS's architectural difficulties and emerging tendencies.