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Passwordless authentication was first tested for seamless and secure merchant payments without the use of passwords or pins. It opened a whole new world of authentications giving up the former reliance on traditional passwords. It relied on the W3C Web Authentication (WebAuthn) and Client to Authenticator Protocol (CTAP) standards to use the public key cryptosystem to uniquely attest a user's device and then their identity. These standards comprise of the FIDO authentication standard. As the popularity of passwordless is increasing, more and more users and service providers are adopting to it. However, the concept of device attestation makes it device-specific for a user. It makes it difficult for a user to switch devices. FIDO Passkeys were aimed at solving the same, synchronizing the private cryptographic keys across multiple devices so that the user can perform passwordless authentication even from devices not explicitly enrolled with the service provider. However, passkeys have certain drawbacks including that it uses proprietary end to end encryption algorithms, all keys pass through proprietary cloud provider, and it is usually not very seamless when dealing with cross-platform key synchronization. To deal with the problems and drawbacks of FIDO Passkeys, the paper proposes a novel private key management system for passwordless authentication called Transferable User Secret on Hardware Key (TUSH-Key). TUSH-Key allows cross-platform synchronization of devices for seamless passwordless logins with FIDO2 specifications.

Smart city solutions require innovative governance approaches together with the smart use of technology, such as digital twins, by city managers and policymakers to manage the big societal challenges. The project Smart Cities aNd Digital Twins in Lower Austria (SCiNDTiLA) extends the state of the art of research in several contributing disciplines and uses the foundations of complexity theory and computational social science methods to develop a digital-twin-based smart city model. The project will also apply a novel transdisciplinary process to conceptualise sustainable smart cities and validate the smart city generic model. The outcomes will be translated into a roadmap highlighting methodologies, guidelines and policy recommendations for tackling societal challenges in smart cities with a focus on rescaling the entire framework to be transferred to regions, smaller towns and non-urban environments, such as rural areas and smart villages, in ways that fit the respective local governance, ethical and operational capacity context.

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.

Money is more than just a numeric value. It embodies trust and moral gravity, and it offers flexible ways to transact. However, the emergence of Central Bank Digital Currency (CBDC) is set to bring about a drastic change in the future of money. This paper invites designers to reflect on their role in shaping material and immaterial monetary change. In this rapidly changing landscape, design could be instrumental in uncovering and showcasing the diverse values that money holds for different stakeholders. Understanding these diversities could promote a more equitable and inclusive financial, social, and global landscape within emergent forms of cash-like digital currency. Without such consideration, certain forms of money we have come to know could disappear, along with the values people hold upon them. We report on semi-structured interviews with stakeholders who have current knowledge or involvement in the emerging field of Central Bank Digital Currency (CBDC). Our research indicates that this new form of money presents both challenges and opportunities for designers. Specifically, we emphasise the potential for Central Bank Digital Currency (CBDC) to either positively or negatively reform values through its design. By considering time, reflecting present values, and promoting inclusion in its deployment, we can strive to ensure that Central Bank Digital Currency (CBDC) represents the diverse needs and perspectives of its users.

Recent reinforcement learning (RL) methods have achieved success in various domains. However, multi-agent RL (MARL) remains a challenge in terms of decentralization, partial observability and scalability to many agents. Meanwhile, collective behavior requires resolution of the aforementioned challenges, and remains of importance to many state-of-the-art applications such as active matter physics, self-organizing systems, opinion dynamics, and biological or robotic swarms. Here, MARL via mean field control (MFC) offers a potential solution to scalability, but fails to consider decentralized and partially observable systems. In this paper, we enable decentralized behavior of agents under partial information by proposing novel models for decentralized partially observable MFC (Dec-POMFC), a broad class of problems with permutation-invariant agents allowing for reduction to tractable single-agent Markov decision processes (MDP) with single-agent RL solution. We provide rigorous theoretical results, including a dynamic programming principle, together with optimality guarantees for Dec-POMFC solutions applied to finite swarms of interest. Algorithmically, we propose Dec-POMFC-based policy gradient methods for MARL via centralized training and decentralized execution, together with policy gradient approximation guarantees. In addition, we improve upon state-of-the-art histogram-based MFC by kernel methods, which is of separate interest also for fully observable MFC. We evaluate numerically on representative collective behavior tasks such as adapted Kuramoto and Vicsek swarming models, being on par with state-of-the-art MARL. Overall, our framework takes a step towards RL-based engineering of artificial collective behavior via MFC.

We introduce SCIO*, a formally secure compilation framework for statically verified partial programs performing input-output (IO). The source language is an F* subset in which a verified program interacts with its IO-performing context via a higher-order interface that includes refinement types as well as pre- and post-conditions about past IO events. The target language is a smaller F* subset in which the compiled program is linked with an adversarial context that has an interface without refinement types, pre-conditions, or concrete post-conditions. To bridge this interface gap and make compilation and linking secure we propose a formally verified combination of higher-order contracts and reference monitoring for recording and controlling IO operations. Compilation uses contracts to convert the logical assumptions the program makes about the context into dynamic checks on each context-program boundary crossing. These boundary checks can depend on information about past IO events stored in the state of the monitor. But these checks cannot stop the adversarial target context before it performs dangerous IO operations. Therefore linking in SCIO* additionally forces the context to perform all IO actions via a secure IO library, which uses reference monitoring to dynamically enforce an access control policy before each IO operation. We prove in F* that SCIO* soundly enforces a global trace property for the compiled verified program linked with the untrusted context. Moreover, we prove in F* that SCIO* satisfies by construction Robust Relational Hyperproperty Preservation, a very strong secure compilation criterion. Finally, we illustrate SCIO* at work on a simple web server example.

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as \emph{decentralized structure}, \emph{information-centric routing} and \emph{semantic communications} entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT.

Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces. These characteristics have attracted the artificial intelligence (AI) community by supporting development of algorithms with complex benchmarks and the capability to rapidly iterate over new ideas. The success of artificial intelligence algorithms in real-time strategy games such as StarCraft II have also attracted the attention of the military research community aiming to explore similar techniques in military counterpart scenarios. Aiming to bridge the connection between games and military applications, this work discusses past and current efforts on how games and simulators, together with the artificial intelligence algorithms, have been adapted to simulate certain aspects of military missions and how they might impact the future battlefield. This paper also investigates how advances in virtual reality and visual augmentation systems open new possibilities in human interfaces with gaming platforms and their military parallels.

Deep Learning (DL) is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical applications. However, it is now known that DL is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos. Since the discovery of this phenomenon in 2013~[1], it has attracted significant attention of researchers from multiple sub-fields of machine intelligence. In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018. Many of those contributions have inspired new directions in this area, which has matured significantly since witnessing the first generation methods. Hence, as a legacy sequel of [2], this literature review focuses on the advances in this area since 2018. To ensure authenticity, we mainly consider peer-reviewed contributions published in the prestigious sources of computer vision and machine learning research. Besides a comprehensive literature review, the article also provides concise definitions of technical terminologies for non-experts in this domain. Finally, this article discusses challenges and future outlook of this direction based on the literature reviewed herein and [2].

As data are increasingly being stored in different silos and societies becoming more aware of data privacy issues, the traditional centralized training of artificial intelligence (AI) models is facing efficiency and privacy challenges. Recently, federated learning (FL) has emerged as an alternative solution and continue to thrive in this new reality. Existing FL protocol design has been shown to be vulnerable to adversaries within or outside of the system, compromising data privacy and system robustness. Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries. In this paper, we conduct the first comprehensive survey on this topic. Through a concise introduction to the concept of FL, and a unique taxonomy covering: 1) threat models; 2) poisoning attacks and defenses against robustness; 3) inference attacks and defenses against privacy, we provide an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks and defenses. Finally, we discuss promising future research directions towards robust and privacy-preserving federated learning.