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Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in federated learning. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual format. The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models. To address the risk of misclassifications with LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several rounds of communication. Extensive tests of LayerCAM-AE on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with existing ResNet-50 and REGNETY-800MF defense models. Experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and test accuracy in FL, surpassing the performance of both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE

This paper critically evaluates the HESA (Higher Education Statistics Agency) Data Report for the Employer Justified Retirement Age (EJRA) Review Group at the University of Cambridge (\cite{CambridgeHESA2024}), identifying significant methodological flaws and misinterpretations. Our analysis reveals issues such as unclear application of data filters, inconsistent variable treatment, and erroneous statistical conclusions. The Report suggests that the EJRA increased job creation rates at Cambridge, but we show Cambridge consistently had lower job creation rates for Established Academic Careers compared to other Russell Group universities, both before and after EJRA implementation in 2011, with no evidence for a significant change in this deficit post implementation. This suggests that EJRA is not a significant factor driving job creation rates. Since other universities without an EJRA exhibit higher job creation rates, this suggests job creation can be sustained without such a policy. We conclude that the EJRA did not achieve its intended goal of increasing opportunities for young academics and may have exacerbated existing disparities compared to other leading universities. We recommend EJRA be abolished at Cambridge since it does not meet its aims and could be viewed as unlawful age discrimination.\newline \it{This version is a revision reflecting some of the comments made by members of the university EJRA review group in public discussion, see \cite{Holmes24}.}, Comment: This is a revision of our earlier paper

This study addresses the security challenges associated with the current internet transformations, specifically focusing on emerging technologies such as blockchain and decentralized storage. It also investigates the role of Web3 applications in shaping the future of the internet. The primary objective is to propose a novel design for 'smart certificates,' which are digital certificates that can be programmatically enforced. Utilizing such certificates, an enterprise can better protect itself from cyberattacks and ensure the security of its data and systems. Web3 recent security solutions by companies and projects like Certik, Forta, Slither, and Securify are the equivalent of code scanning tool that were originally developed for Web1 and Web2 applications, and definitely not like certificates to help enterprises feel safe against cyberthreats. We aim to improve the resilience of enterprises' digital infrastructure by building on top of Web3 application and put methodologies in place for vulnerability analysis and attack correlation, focusing on architecture of different layers, Wallet/Client, Application and Smart Contract, where specific components are provided to identify and predict threats and risks. Furthermore, Certificate Transparency is used for enhancing the security, trustworthiness and decentralized management of the certificates, and detecting misuses, compromises, and malfeasances.

Blockchain technology has revolutionized the way information is propagated in decentralized networks. Ethereum plays a pivotal role in facilitating smart contracts and decentralized applications. Understanding information propagation dynamics in Ethereum is crucial for ensuring network efficiency, security, and scalability. In this study, we propose an innovative approach that utilizes Graph Convolutional Networks (GCNs) to analyze the information propagation patterns in the Ethereum network. The first phase of our research involves data collection from the Ethereum blockchain, consisting of blocks, transactions, and node degrees. We construct a transaction graph representation using adjacency matrices to capture the node embeddings; while our major contribution is to develop a combined Graph Attention Network (GAT) and Reinforcement Learning (RL) model to optimize the network efficiency and scalability. It learns the best actions to take in various network states, ultimately leading to improved network efficiency, throughput, and optimize gas limits for block processing. In the experimental evaluation, we analyze the performance of our model on a large-scale Ethereum dataset. We investigate effectively aggregating information from neighboring nodes capturing graph structure and updating node embeddings using GCN with the objective of transaction pattern prediction, accounting for varying network loads and number of blocks. Not only we design a gas limit optimization model and provide the algorithm, but also to address scalability, we demonstrate the use and implementation of sparse matrices in GraphConv, GraphSAGE, and GAT. The results indicate that our designed GAT-RL model achieves superior results compared to other GCN models in terms of performance. It effectively propagates information across the network, optimizing gas limits for block processing and improving network efficiency.

Redactable signature schemes and sanitizable signature schemes are methods that permit modification of a given digital message and retain a valid signature. This can be applied to decentralized identity systems for delegating identity issuance and redacting sensitive information for privacy-preserving verification of identity. We propose implementing these protocols on a digital credential and compare them against other privacy-enhancing techniques to assess their suitability, Comment: Extended Abstract, 3 Pages, 1 Figure, International Conference on AI and the Digital Economy 2023

In recent years, the maturation of emerging technologies such as Virtual Reality, Digital twins, and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities that bring great convenience to society. In addition, the metaverse can facilitate digital twins, which offers transformative possibilities for the industry. Thus, the metaverse has attracted the attention of the industry, and a huge amount of capital is about to be invested. However, the development of the metaverse is still in its infancy and little research has been undertaken so far. We describe the development of the metaverse. Next, we introduce the architecture of the socially beneficial metaverse (SB-Metaverse) and we focus on the technologies that support the operation of SB-Metaverse. In addition, we also present the applications of SB-Metaverse. Finally, we discuss several challenges faced by SB-Metaverse which must be addressed in the future., Comment: 28 pages, 6 figures, 3 tables

Many countries have embraced national electronic identification (NeID) systems, recognising their potential to foster a fair, transparent, and well-governed society by ensuring the secure verification of citizens' identities. The inclusive nature of NeID empowers people to exercise their rights while holding them accountable for fulfilling their obligations. Nevertheless, the development and implementation of these complex identity-verification systems have raised concerns regarding security, privacy, and exclusion. In this study, we discuss the different categories of NeID risk and explore the successful deployment of these systems, while examining how the specific risks and other challenges posed by this technology are addressed. Based on the review of the different NeID systems and the efforts made to mitigate the unique risks and challenges presented within each deployment, we highlighted the best practices for mitigating risk, including implementing strong security measures, conducting regular risk assessments, and involving stakeholders in the design and implementation of the system., Comment: 6 pages

Electronic identification (eID) systems allow citizens to assert and authenticate their identities for various purposes, such as accessing government services or conducting financial transactions. These systems improve user access to rights, services, and the formal economy. As eID systems become an essential facet of national development, any failure, compromise, or misuse can be costly and damaging to the government, users, and society. Therefore, an effective risk assessment is vital for identifying emerging risks to the system and assessing their impact. However, developing a comprehensive risk assessment for these systems must extend far beyond focusing on technical security and privacy impacts and must be conducted with a contextual understanding of stakeholders and the communities these systems serve. In this study, we posit that current risk assessments do not address risk factors for all key stakeholders and explore how potential compromise could impact them each in turn. In the examination of the broader impact of risks and the potentially significant consequences for stakeholders, we propose a framework that considers a wide range of factors, including the social, economic, and political contexts in which these systems were implemented. This provides a holistic platform for a better assessment of risk to the eID system., Comment: 10 pages

Redactable Signature Schemes and Zero-Knowledge Proofs are two radically different approaches to enable privacy. This paper analyses their merits and drawbacks when applied to decentralized identity system. Redactable Signatures, though competitively quick and compact, are not as expressive as zero-knowledge proofs and do not provide the same level of privacy. On the other hand, zero-knowledge proofs can be much faster but some protocols require a trusted set-up. We conclude that given the benefits and drawbacks, redactable signatures are more appropriate at an earlier stage and zero-knowledge proofs are more appropriate at a later stage for decentralized identity systems, Comment: 9 Pages, Trustworthy digital identity international conference 2023

The rapid evolution of the Ethereum network necessitates sophisticated techniques to ensure its robustness against potential threats and to maintain transparency. While Graph Neural Networks (GNNs) have pioneered anomaly detection in such platforms, capturing the intricacies of both spatial and temporal transactional patterns has remained a challenge. This study presents a fusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) enhanced by probabilistic sampling to bridge this gap. Our approach, unlike traditional GCNs, leverages the strengths of TRW to discern complex temporal sequences in Ethereum transactions, thereby providing a more nuanced transaction anomaly detection mechanism. Preliminary evaluations demonstrate that our TRW-GCN framework substantially advances the performance metrics over conventional GCNs in detecting anomalies and transaction bursts. This research not only underscores the potential of temporal cues in Ethereum transactional data but also offers a scalable and effective methodology for ensuring the security and transparency of decentralized platforms. By harnessing both spatial relationships and time-based transactional sequences as node features, our model introduces an additional layer of granularity, making the detection process more robust and less prone to false positives. This work lays the foundation for future research aimed at optimizing and enhancing the transparency of blockchain technologies, and serves as a testament to the significance of considering both time and space dimensions in the ever-evolving landscape of the decentralized platforms.

IoT is a fundamental enabling technology for creating smart spaces, which can assist the effective face-to-face and online education systems. The transition to smart education (integrating IoT and AI into the education system) is appealing, which has a concrete impact on learners' engagement, motivation, attendance, and deep learning. Traditional education faces many challenges, including administration, pedagogy, assessment, and classroom supervision. Recent developments in ICT (e.g., IoT, AI and 5G, etc.) have yielded lots of smart solutions for various aspects of life; however, smart solutions are not well integrated into the education system. In particular, the COVID-19 pandemic situation had further emphasized the adoption of new smart solutions in education. This study reviews the related studies and addresses the (i) problems in the traditional education system with possible solutions, (ii) the transition towards smart education, and (iii) research challenges in the transition to smart education (i.e, computational and social resistance). Considering these studies, smart solutions (e.g., smart pedagogy, smart assessment, smart classroom, smart administration, etc.) are introduced to the problems of the traditional system. This exploratory study opens new trends for scholars and the market to integrate ICT, IoT, and AI into smart education., Comment: 30 pages, 16 tables, 6 figures. This article is accepted for publication in ACM Computing Surveys

We study a cache network in which intermediate nodes equipped with caches can serve requests. We model the problem of jointly optimizing caching and routing decisions with link capacity constraints over an arbitrary network topology. This problem can be formulated as a continuous diminishing-returns (DR) submodular maximization problem under multiple continuous DR-supermodular constraints, and is NP-hard. We propose a poly-time alternating primal-dual heuristic algorithm, in which primal steps produce solutions within $1-\frac{1}{e}$ approximation factor from the optimal. Through extensive experiments, we demonstrate that our proposed algorithm significantly outperforms competitors., Comment: Extended version of 'Jointly Optimal Routing and Caching with Bounded Link Capacities' accepted by ICC 2023

Multicast is (almost) as old as the Internet, having become a tool for increasing network efficiency but also enabling destination discovery in a number of key use cases, although misaligned economic interests have limited its deployment to domain-local usages. But recent advances in multicast technologies as well as the identification of new use cases for which IP multicast may be ill fitted yet network-level support may be desirable motivate to re-think old perceptions of multicast and its use in the Internet overall. For this, we return to the original question to which multicast is seemingly the right answer, based on which we outline emerging new answers to what multicast intends to achieve. Key to this is to re-formulate the multicast question in an attempt to semantically and architecturally align different answers, opening opportunities for more use cases to be served through multicast solutions, thus also driving the need for more research in this space. Our paper poses this new vision for multicast and investigates the alignment of existing and emerging multicast solutions with it, leading us to formulate a path for future research., Comment: Under discussion

Not all research leads to fruitful results; trying new ways or methods may surpass the state of the art, but sometimes the hypothesis is not proven or the improvement is insignificant. In a systems discipline like pervasive computing, there are many sources of errors, from hardware issues over communication channels to heterogeneous software environments. However, failure to succeed is not a failure to progress. It is essential to create platforms for sharing insights, experiences, and lessons learned when conducting research in pervasive computing so that the same mistakes are not repeated. And sometimes, a problem is a symptom of discovering new research challenges. Based on the collective input of the First International Workshop on Negative Results in Pervasive Computing (PerFail 2022), co-located with the 20th International Conference on Pervasive Computing and Communications (PerCom 2022), this paper presents a comprehensive discussion on perspectives on publishing negative results and lessons learned in pervasive computing.

Mobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications. Traditional ML techniques have shifted toward new paradigms such as federated (FL) and split learning (SL) to improve the protection of user's data privacy. However, these paradigms often rely on server(s) located in the edge or cloud to train computationally-heavy parts of a ML model to avoid draining the limited resource on client devices, resulting in exposing device data to such third parties. This work proposes an alternative approach to train computationally-heavy ML models in user's devices themselves, where corresponding device data resides. Specifically, we focus on GANs (generative adversarial networks) and leverage their inherent privacy-preserving attribute. We train the discriminative part of a GAN with raw data on user's devices, whereas the generative model is trained remotely (e.g., server) for which there is no need to access sensor true data. Moreover, our approach ensures that the computational load of training the discriminative model is shared among user's devices-proportional to their computation capabilities-by means of SL. We implement our proposed collaborative training scheme of a computationally-heavy GAN model in real resource-constrained devices. The results show that our system preserves data privacy, keeps a short training time, and yields same accuracy of model training in unconstrained devices (e.g., cloud). Our code can be found on https://github.com/YukariSonz/FSL-GAN

This work performs the statistical QoS analysis of a Rician block-fading reconfigurable intelligent surface (RIS)-assisted D2D link in which the transmit node operates under delay QoS constraints. First, we perform mode selection for the D2D link, in which the D2D pair can either communicate directly by relaying data from RISs or through a base station (BS). Next, we provide closed-form expressions for the effective capacity (EC) of the RIS-assisted D2D link. When channel state information at the transmitter (CSIT) is available, the transmit D2D node communicates with the variable rate $r_t(n)$ (adjustable according to the channel conditions); otherwise, it uses a fixed rate $r_t$. It allows us to model the RIS-assisted D2D link as a Markov system in both cases. We also extend our analysis to overlay and underlay D2D settings. To improve the throughput of the RIS-assisted D2D link when CSIT is unknown, we use the HARQ retransmission scheme and provide the EC analysis of the HARQ-enabled RIS-assisted D2D link. Finally, simulation results demonstrate that: i) the EC increases with an increase in RIS elements, ii) the EC decreases when strict QoS constraints are imposed at the transmit node, iii) the EC decreases with an increase in the variance of the path loss estimation error, iv) the EC increases with an increase in the probability of ON states, v) EC increases by using HARQ when CSIT is unknown, and it can reach up to $5\times$ the usual EC (with no HARQ and without CSIT) by using the optimal number of retransmissions., Comment: Accepted for publication in IEEE Transactions on Vehicular Technology

Given a simple concept that has the potential for a methodological contribution to the field of HCI, the constraints of submission conventions within the field may make it impossible to communicate the concept in a manner that is intelligible to the reader., Comment: 9 pages

We introduce Proof-of-Steak (PoS) as a fundamental net-zero block generation technique, often accompanied by Non-Frangipane Tokens. Genesis cut is gradually heated and minted (using the appropriate sauce), enabling the miners to redirect the extracted gold and the dissipated heat into the furnace, hence enabling the first fully-circular economy ever built using blockchain technology, utilising tamper-evident steak hach\'e. In this paper we present the basic ingredients for building Proof-of-Steak, assessing its global impact, and opportunities to save the world and beyond!, Comment: This is a silly article

In developing regions a substantial number of users rely on legacy and ultra-low-cost mobile devices. Unfortunately, many of these devices are not equipped to run the standard authentication or identity apps that are available for smartphones. Increasingly, apps that display Quick Response (QR) codes are being used to communicate personal credentials (e.g., Covid-19 vaccination certificates). This paper describes a novel interface for QR code credentials that is compatible with legacy mobile devices. Our solution, which we have released under open source licensing, allows Web Application Enabled legacy mobile devices to load and display standard QR codes. This technique makes modern identity platforms available to previously excluded and economically disadvantaged populations.

This work does the statistical quality-of-service (QoS) analysis of a block-fading device-to-device (D2D) link in a multi-tier cellular network that consists of a macro-BS (BSMC) and a micro-BS (BSmC) which both operate in full-duplex (FD) mode. For the D2D link under consideration, we first formulate the mode selection problem-whereby D2D pair could either communicate directly, or, through the BSmC, or, through the BSMC-as a ternary hypothesis testing problem. Next, to compute the effective capacity (EC) for the given D2D link, we assume that the channel state information (CSI) is not available at the transmit D2D node, and hence, it transmits at a fixed rate r with a fixed power. This allows us to model the D2D link as a Markov system with six-states. We consider both overlay and underlay modes for the D2D link. Moreover, to improve the throughput of the D2D link, we assume that the D2D pair utilizes two special automatic repeat request (ARQ) schemes, i.e., Hybrid-ARQ (HARQ) and truncated HARQ. Furthermore, we consider two distinct queue models at the transmit D2D node, based upon how it responds to the decoding failure at the receive D2D node. Eventually, we provide closed-form expressions for the EC for both HARQ-enabled D2D link and truncated HARQ-enabled D2D link, under both queue models. Noting that the EC looks like a quasi-concave function of r, we further maximize the EC by searching for an optimal rate via the gradient-descent method. Simulation results provide us the following insights: i) EC decreases with an increase in the QoS exponent, ii) EC of the D2D link improves when HARQ is employed, iii) EC increases with an increase in the quality of self-interference cancellation techniques used at BSmC and BSMC in FD mode., Comment: Accepted for publication in IEEE Transactions on Vehicular Technology

In this paper, we provide (i) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies, and (ii)a real-data driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails.To the best of our knowledge, this is the only complete general theory till date on the feasibility of aggregate cyber-risk management., Comment: incrementally updated version to version in IEEE Internet of Things Journal

In the modern world, the connectivity-as-we-go model is gaining popularity. Internet-of-Things (IoT) envisions a future in which human beings communicate with each other and with devices that have identities and virtual personalities, as well as sensing, processing, and networking capabilities, which will allow the developing of smart environments that operate with little or no human intervention. In such IoT environments, that will have battery-operated sensors and devices, energy efficiency becomes a fundamental concern. Thus, energy-efficient (EE) connectivity is gaining significant attention from the industrial and academic communities. This work aims to provide a comprehensive state-of-the-art survey on the energy efficiency of medium access control (MAC) protocols for cellular IoT. we provide a detailed discussion on the sources of energy dissipation at the MAC layer and then propose solutions. In addition to reviewing the proposed MAC designs, we also provide insights and suggestions that can guide practitioners and researchers in designing EE MAC protocols that extend the battery life of IoT devices. Finally, we identify a range of challenging open problems that should be solved for providing EE MAC services for IoT devices, along with corresponding opportunities and future research ideas to address these challenges.

The mobile communication system has transformed to be the fundamental infrastructure to support digital demands from all industry sectors, and 6G is envisioned to go far beyond the communication-only purpose. There is coming to a consensus that 6G will treat Artificial Intelligence (AI) as the cornerstone and has a potential capability to provide "intelligence inclusion", which implies to enable the access of AI services at anytime and anywhere by anyone. Apparently, the intelligent inclusion vision produces far-reaching influence on the corresponding network architecture design in 6G and deserves a clean-slate rethink. In this article, we propose an end-to-end system architecture design scope for 6G, and talk about the necessity to incorporate an independent data plane and a novel intelligent plane with particular emphasis on end-to-end AI workflow orchestration, management and operation. We also highlight the advantages to provision converged connectivity and computing services at the network function plane. Benefiting from these approaches, we believe that 6G will turn to an "everything as a service" (XaaS) platform with significantly enhanced business merits.

Knowledge of Wi-Fi networks helps to guide future engineering and spectrum policy decisions. However, due to its unlicensed nature, the deployment of Wi-Fi Access Points is undocumented meaning researchers are left making educated guesses as to the prevalence of these assets through remotely collected or passively sensed measurements. One commonly used method is referred to as `wardriving` essentially where a vehicle is used to collect geospatial statistical data on wireless networks to inform mobile computing and networking security research. Surprisingly, there has been very little examination of the statistical issues with wardriving data, despite the vast number of analyses being published in the literature using this approach. In this paper, a sample of publicly collected wardriving data is compared to a predictive model for Wi-Fi Access Points. The results demonstrate several statistical issues which future wardriving studies must account for, including selection bias, sample representativeness and the modifiable areal unit problem.

This paper argues that data of strategic individuals with heterogeneous privacy valuations in a distributed online social network (e.g., Facebook) will be under-priced, if traded in a monopoly buyer setting, and will lead to diminishing utilitarian welfare. This result, for a certain family of online community data trading problems, is in stark contrast to a popular information economics intuition that increased amounts of end-user data signals in a data market improves its efficiency. Our proposed theory paves the way for a future (counter-intuitive) analysis of data trading oligopoly markets for online social networks (OSNs)., Comment: incrementally updated version to version in IEEE Networking Letters; This work is based upon results in NBER w26296

The hype about Bitcoin has overrated the potential of smart contracts deployed on-blockchains (on-chains) and underrated the potential of smart contracts deployed on-Trusted Third Parties (on-TTPs). As a result, current research and development in this field is focused mainly on smart contract applications that use on-chain smart contracts. We argue that there is a large class of smart contract applications where on-TTP smart contracts are a better alternative. The problem with on-chain smart contracts is that the fully decentralised model and indelible append-only data model followed by blockchains introduces several engineering problems that are hard to solve. In these situations, the inclusion of a TTP (assuming that the application can tolerate its inconveniences) instead of a blockchain to host the smart contract simplifies the problems and offers pragmatic solutions. The intention and contribution of this paper is to shed some light on this issue. We use a hypothetical use case of a car insurance application to illustrate technical problems that are easier to solve with on-TTP smart contracts than with on-chain smart contracts., Comment: The document consists of 14 pages and includes four figures, one table and 76 references. It the work of five authors. Carlos Molina-Jimenez is the corresponding and responsible author

We suggest the re-introduction of bartering to create a cryptocurrencyless, currencyless, and moneyless economy segment. We contend that a barter economy would benefit enterprises, individuals, governments and societies. For instance, the availability of an online peer-to-peer barter marketplace would convert ordinary individuals into potential traders of both tangible and digital items and services. For example, they will be able to barter files and data that they collect. Equally motivating, they will be able to barter and re-introduce to the economy items that they no longer need such as, books, garden tools, and bikes which are normally kept and wasted in garages and sheds. We argue that most of the pieces of technology needed for building a barter system are now available, including blockchains, smart contracts, cryptography, secure multiparty computations and fair exchange protocols. However, additional research is needed to refine and integrate the pieces together. We discuss potential research directions., Comment: The document consists of 22 pages in total, including references and two figures. The author list has five authors

On May $28^{th}$ and $29^{th}$, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in Network Science and Epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in Network Science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found., Comment: Ayan Paul references preprints (DESY 20-142, HU-EP-20/23)

In the fight against Covid-19, many governments and businesses are in the process of evaluating, trialling and even implementing so-called immunity passports. Also known as antibody or health certificates, there is a clear demand for any technology that could allow people to return to work and other crowded places without placing others at risk. One of the major criticisms of such systems is that they could be misused to unfairly discriminate against those without immunity, allowing the formation of an `immuno-privileged' class of people. In this work we are motivated to explore an alternative technical solution that is non-discriminatory by design. In particular we propose health tokens -- randomised health certificates which, using methods from differential privacy, allow individual test results to be randomised whilst still allowing useful aggregate risk estimates to be calculated. We show that health tokens could mitigate immunity-based discrimination whilst still presenting a viable mechanism for estimating the collective transmission risk posed by small groups of users. We evaluate the viability of our approach in the context of identity-free and identity-binding use cases and then consider a number of possible attacks. Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0.03 on average and thus the aggregated results can be useful in a number of identity-free contexts. Finally, we present the results of our open-source prototype which demonstrates the practicality of our solution.

COVID-19 has resulted in unprecedented social distancing policies being enforced worldwide. As governments seek to restore their economies, open workplaces and permit travel there is a demand for technologies that may alleviate the requirement for social distancing whilst also protecting healthcare services. In this work we explore the controversial technique of so-called immunity passports and present SecureABC: a decentralised, privacy-preserving protocol for issuing and verifying antibody certificates. We consider the implications of antibody certificate systems, develop a set of risk-minimising principles and a security framework for their evaluation, and show that these may be satisfied in practice. Finally, we also develop two additional protocols that minimise individual discrimination but which still allow for collective transmission risk to be estimated. We use these two protocols to illustrate the utility-privacy trade-offs of antibody certificates and their alternatives.

Due to the advancement of IoT devices in both domestic and industrial environments, the need to incorporate a mechanism to build accountability in the IoT ecosystem is paramount. In the last few years, various initiatives have been started in this direction addressing many socio-technical concerns and challenges to build an accountable system. The solution that has received a lot of attention in both industry and academia is the Manufacturer Usage Description (MUD) specification. It gives the possibility to the IoT device manufacturers to describe communications needed by each device to work properly. MUD implementation is challenging not only due to the diversity of IoT devices and manufacturer/operator/regulators but also due to the incremental integration of MUD-based flow control in the already existing Internet infrastructure. To provide a better understanding of these challenges, in this work, we explore and investigate the prototypes of three implementations proposed by different research teams and organisations, useful for the community to understand which are the various features implemented by the existing technologies. By considering that there exist some behaviours which can be only defined by local policy, we propose a MUD capable network integrating our User Policy Server(UPS). The UPS provides network administrators and endusers an opportunity to interact with MUD components through a user-friendly interface. Hence, we present a comprehensive survey of the challenges., Comment: 7 pages, 5 figures, WIP

The cellular internet of things (CIoT) has become an important branch to cater various applications of IoT devices. Within CIoT, the radio resource control (RRC) layer is responsible for fundamental functionalities such as connection control and bearer establishment in radio access network (RAN). The emergence of various IoT scenarios and diversified service requirements have made both RAN slicing and intelligent control imperative requirement in RRC layer. This paper focuses on enhancing standardized capabilities of CIoT RRC layer, by designing and implementing a new architecture which accommodate RRC slicing and intelligent controller. The architecture aims to realize functionalities of creating, modifying, and deleting slices in RRC layer, while the intelligent controller is added to satisfy various and dynamic service requirements of different IoT devices smartly. The proposed architecture is further implemented on an open-source software platform OpenAirInterface (OAI), on top of which the effectiveness of RRC slicing is validated and one proof-of-concept case to adopt reinforcement learning to dynamically tune discontinuous reception parameters therein is presented. Simulation results have demonstrated the effectiveness of the proposed intelligent RRC slicing architecture., Comment: Submitted to IEEE Internet of Things Journal

Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns, however, regarding the privacy and security of users data when using these apps. These concerns are paramount for users who contract the virus, as they are generally required to release all their data. Motivated by the need to protect users privacy we propose two solutions to this problem. Our first solution builds on current "message based" methods and our second leverages ideas from secret sharing and additively homomorphic encryption.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions., Comment: 53 pages, 37 figures, survey

In this paper we propose SCDP, a general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, supports efficient one-to-many and many-to-one communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, packet trimming and Multi-Level Feedback Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many-to-one data transport; (2) on top of RaptorQ codes, receiver-driven flow control, in combination with in-network packet trimming, enable efficient usage of network resources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluate SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better compared to NDP and PIAS. For short and long unicast flows, SCDP performs equally well or better compared to NDP and PIAS., Comment: 14 pages, 17 figures, Submitted to ToN Journal

We present a federated, asynchronous, and $(\varepsilon, \delta)$-differentially private algorithm for PCA in the memory-limited setting. Our algorithm incrementally computes local model updates using a streaming procedure and adaptively estimates its $r$ leading principal components when only $\mathcal{O}(dr)$ memory is available with $d$ being the dimensionality of the data. We guarantee differential privacy via an input-perturbation scheme in which the covariance matrix of a dataset $\mathbf{X} \in \mathbb{R}^{d \times n}$ is perturbed with a non-symmetric random Gaussian matrix with variance in $\mathcal{O}\left(\left(\frac{d}{n}\right)^2 \log d \right)$, thus improving upon the state-of-the-art. Furthermore, contrary to previous federated or distributed algorithms for PCA, our algorithm is also invariant to permutations in the incoming data, which provides robustness against straggler or failed nodes. Numerical simulations show that, while using limited-memory, our algorithm exhibits performance that closely matches or outperforms traditional non-federated algorithms, and in the absence of communication latency, it exhibits attractive horizontal scalability., Comment: 36 pages, 13 figures, 1 table. Accepted for publication at Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada

Motivated by the huge disparity between the limited battery capacity of user devices and the ever-growing energy demands of modern mobile apps, we propose INFv. It is the first offloading system able to cache, migrate and dynamically execute on demand functionality from mobile devices in ISP networks. It aims to bridge this gap by extending the promising NFV paradigm to mobile applications in order to exploit in-network resources. In this paper, we present the overall design, state-of-the-art technologies adopted, and various engineering details in the INFv system. We also carefully study the deployment configurations by investigating over 20K Google Play apps, as well as thorough evaluations with realistic settings. In addition to a significant improvement in battery life (up to 6.9x energy reduction) and execution time (up to 4x faster), INFv has two distinct advantages over previous systems: 1) a non-intrusive offloading mechanism transparent to existing apps; 2) an inherent framework support to effectively balance computation load and exploit the proximity of in-network resources. Both advantages together enable a scalable and incremental deployment of computation offloading framework in practical ISPs' networks.

This study uses the article content and metadata of four important computer networking periodicals-IEEE Communications Surveys and Tutorials (COMST), IEEE/ACM Transactions on Networking (TON), ACM Special Interest Group on Data Communications (SIGCOMM), and IEEE International Conference on Computer Communications (INFOCOM)-obtained using ACM, IEEE Xplore, Scopus and CrossRef, for an 18-year period (2000-2017) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The first two of these periodicals (COMST and TON) are highly reputed journals of the fields while SIGCOMM and INFOCOM are considered top conferences of the field. SIGCOMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of different research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the co-evolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) content-based analysis; and (c) citation analysis. In addition, we identify the significant trends and the most influential authors, institutes and countries, based on the publication count as well as article citations. Through this study, we are proposing a methodology and framework for performing a comprehensive bibliometric analysis on computer networking research. To the best of our knowledge, no such study has been undertaken in computer networking until now., Comment: 36 Pages, Currently submitted to Springer Scientometrics Journal 2019

Blockchain is challenging the status quo of the central trust infrastructure currently prevalent in the Internet towards a design principle that is underscored by decentralization, transparency, and trusted auditability. In ideal terms, blockchain advocates a decentralized, transparent, and more democratic version of the Internet. Essentially being a trusted and decentralized database, blockchain finds its applications in fields as varied as the energy sector, forestry, fisheries, mining, material recycling, air pollution monitoring, supply chain management, and their associated operations. In this paper, we present a survey of blockchain-based network applications. Our goal is to cover the evolution of blockchain-based systems that are trying to bring in a renaissance in the existing, mostly centralized, space of network applications. While re-imagining the space with blockchain, we highlight various common challenges, pitfalls, and shortcomings that can occur. Our aim is to make this work as a guiding reference manual for someone interested in shifting towards a blockchain-based solution for one's existing use case or automating one from the ground up., Comment: Under Review in IEEE COMST

Decentralization, in the form of mesh networking and blockchain, two promising technologies, is coming to the telecommunications industry. Mesh networking allows wider low cost Internet access with infrastructures built from routers contributed by diverse owners, while blockchain enables transparency and accountability for investments, revenue or other forms of economic compensations from sharing of network traffic, content and services. Crowdsourcing network coverage, combined with crowdfunding costs, can create economically sustainable yet decentralized Internet access. This means every participant can invest in resources, and pay or be paid for usage to recover the costs of network devices and maintenance. While mesh networks and mesh routing protocols enable self-organized networks that expand organically, cryptocurrencies and smart contracts enable the economic coordination among network providers and consumers. We explore and evaluate two existing blockchain software stacks, Hyperledger Fabric (HLF) and Ethereum geth with Proof of Authority (PoA) intended as a local lightweight distributed ledger, deployed in a real city-wide production mesh network and also in laboratory network. We quantify the performance, bottlenecks and identify the current limitations and opportunities for improvement to serve locally the needs of wireless mesh networks, without the privacy and economic cost of relying on public blockchains., Comment: arXiv admin note: substantial text overlap with arXiv:1804.00561

Recently, decentralised (on-blockchain) platforms have emerged to complement centralised (off-blockchain) platforms for the implementation of automated, digital (smart) contracts. However, neither alternative can individually satisfy the requirements of a large class of applications. On-blockchain platforms suffer from scalability, performance, transaction costs and other limitations. Off-blockchain platforms are afflicted by drawbacks due to their dependence on single trusted third parties. We argue that in several application areas, hybrid platforms composed from the integration of on- and off-blockchain platforms are more able to support smart contracts that deliver the desired quality of service (QoS). Hybrid architectures are largely unexplored. To help cover the gap, in this paper we discuss the implementation of smart contracts on hybrid architectures. As a proof of concept, we show how a smart contract can be split and executed partially on an off-blockchain contract compliance checker and partially on the Rinkeby Ethereum network. To test the solution, we expose it to sequences of contractual operations generated mechanically by a contract validator tool., Comment: 12 pages, 7 figures

Open-access domestic broadband connection sharing constitutes a voluntary practice that is associated with societal, economic and public-safety benefits. Despite this fact, broadband subscribers are usually hesitant to freely share their broadband connection with guests for a multitude of reasons; one of them being sharing their network might hinder their own broadband quality of experience. In this paper, we investigate experimentally the impact of uplink guest traffic on the sharer's broadband quality of experience under both generic and broadband-sharing-specific packet scheduling policies. Both guest-user traffic and access point profiles employed in our study are developed by analyzing real-world traffic traces and measurements, captured from actual broadband sharing networking environments. Our results validate the suitability of hybrid packet scheduling policies for broadband sharing schemes and show that only a few dozen kilobytes per second of uplink guest traffic can be tolerated by sharers without hampering their broadband quality of experience. In this context, we show that the selection of the most appropriate packet scheduling policy for broadband sharing, as well as its respective configuration, depend largely on the capacity of the broadband connection and the policy's packet-dropping behavior on guest traffic.

In this paper we discuss how conventional business contracts can be converted into smart contracts---their electronic equivalents that can be used to systematically monitor and enforce contractual rights, obligations and prohibitions at run time. We explain that emerging blockchain technology is certainly a promising platform for implementing smart contracts but argue that there is a large class of applications, where blockchain is inadequate due to performance, scalability, and consistency requirements, and also due to language expressiveness and cost issues that are hard to solve. We explain that in some situations a centralised approach that does not rely on blockchain is a better alternative due to its simplicity, scalability, and performance. We suggest that in applications where decentralisation and transparency are essential, developers can advantageously combine the two approaches into hybrid solutions where some operations are enforced by enforcers deployed on--blockchains and the rest by enforcers deployed on trusted third parties.

Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.

The 2nd workshop on Future Internet Architecture for Developing Regions (FI4D) took place on January 8th, 2017. The workshop was hosted by the 14th Annual IEEE Consumer Communications $\&$ Networking Conference in Las Vegas, USA. This report summarizes the motivation, discussions and perspectives held during the half-day event.

Machine Learning (ML) techniques, such as Neural Network, are widely used in today's applications. However, there is still a big gap between the current ML systems and users' requirements. ML systems focus on improving the performance of models in training, while individual users cares more about response time and expressiveness of the tool. Many existing research and product begin to move computation towards edge devices. Based on the numerical computing system Owl, we propose to build the Zoo system to support construction, compose, and deployment of ML models on edge and local devices.