20,984 research outputs found

    Securing NextG networks with physical-layer key generation: A survey

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    As the development of next-generation (NextG) communication networks continues, tremendous devices are accessing the network and the amount of information is exploding. However, with the increase of sensitive data that requires confidentiality to be transmitted and stored in the network, wireless network security risks are further amplified. Physical-layer key generation (PKG) has received extensive attention in security research due to its solid information-theoretic security proof, ease of implementation, and low cost. Nevertheless, the applications of PKG in the NextG networks are still in the preliminary exploration stage. Therefore, we survey existing research and discuss (1) the performance advantages of PKG compared to cryptography schemes, (2) the principles and processes of PKG, as well as research progresses in previous network environments, and (3) new application scenarios and development potential for PKG in NextG communication networks, particularly analyzing the effect and prospects of PKG in massive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), artificial intelligence (AI) enabled networks, integrated space-air-ground network, and quantum communication. Moreover, we summarize open issues and provide new insights into the development trends of PKG in NextG networks

    Edge Computing, IoT and Future Internet

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    In this paper, we discuss researches in Edge Computing, Internet of Things (IoT), and Future Internet Technologies (ICN and NDN). We chose these areas because they are hot topics in today research

    Towards Sybil Resilience in Decentralized Learning

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    Federated learning is a privacy-enforcing machine learning technology but suffers from limited scalability. This limitation mostly originates from the internet connection and memory capacity of the central parameter server, and the complexity of the model aggregation function. Decentralized learning has recently been emerging as a promising alternative to federated learning. This novel technology eliminates the need for a central parameter server by decentralizing the model aggregation across all participating nodes. Numerous studies have been conducted on improving the resilience of federated learning against poisoning and Sybil attacks, whereas the resilience of decentralized learning remains largely unstudied. This research gap serves as the main motivator for this study, in which our objective is to improve the Sybil poisoning resilience of decentralized learning. We present SybilWall, an innovative algorithm focused on increasing the resilience of decentralized learning against targeted Sybil poisoning attacks. By combining a Sybil-resistant aggregation function based on similarity between Sybils with a novel probabilistic gossiping mechanism, we establish a new benchmark for scalable, Sybil-resilient decentralized learning. A comprehensive empirical evaluation demonstrated that SybilWall outperforms existing state-of-the-art solutions designed for federated learning scenarios and is the only algorithm to obtain consistent accuracy over a range of adversarial attack scenarios. We also found SybilWall to diminish the utility of creating many Sybils, as our evaluations demonstrate a higher success rate among adversaries employing fewer Sybils. Finally, we suggest a number of possible improvements to SybilWall and highlight promising future research directions

    Exploring Fully Offloaded GPU Stream-Aware Message Passing

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    Modern heterogeneous supercomputing systems are comprised of CPUs, GPUs, and high-speed network interconnects. Communication libraries supporting efficient data transfers involving memory buffers from the GPU memory typically require the CPU to orchestrate the data transfer operations. A new offload-friendly communication strategy, stream-triggered (ST) communication, was explored to allow offloading the synchronization and data movement operations from the CPU to the GPU. A Message Passing Interface (MPI) one-sided active target synchronization based implementation was used as an exemplar to illustrate the proposed strategy. A latency-sensitive nearest neighbor microbenchmark was used to explore the various performance aspects of the implementation. The offloaded implementation shows significant on-node performance advantages over standard MPI active RMA (36%) and point-to-point (61%) communication. The current multi-node improvement is less (23% faster than standard active RMA but 11% slower than point-to-point), but plans are in progress to purse further improvements.Comment: 12 pages, 17 figure

    Efficient Non-DHT-Based RC-Based Architecture for Fog Computing in Healthcare 4.0

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    Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems

    Strong Invariants Are Hard: On the Hardness of Strongest Polynomial Invariants for (Probabilistic) Programs

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    We show that computing the strongest polynomial invariant for single-path loops with polynomial assignments is at least as hard as the Skolem problem, a famous problem whose decidability has been open for almost a century. While the strongest polynomial invariants are computable for affine loops, for polynomial loops the problem remained wide open. As an intermediate result of independent interest, we prove that reachability for discrete polynomial dynamical systems is Skolem-hard as well. Furthermore, we generalize the notion of invariant ideals and introduce moment invariant ideals for probabilistic programs. With this tool, we further show that the strongest polynomial moment invariant is (i) uncomputable, for probabilistic loops with branching statements, and (ii) Skolem-hard to compute for polynomial probabilistic loops without branching statements. Finally, we identify a class of probabilistic loops for which the strongest polynomial moment invariant is computable and provide an algorithm for it

    Partition of variation for predicting experimental power with a broiler chicken example

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    Conformal dispersion relations for defects and boundaries

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    We derive a dispersion relation for two-point correlation functions in defect conformal field theories. The correlator is expressed as an integral over a (single) discontinuity that is controlled by the bulk channel operator product expansion (OPE). This very simple relation is particularly useful in perturbative settings where the discontinuity is determined by a subset of bulk operators. In particular, we apply it to holographic correlators of two chiral primary operators in N=4\mathcal{N}= 4 Super Yang-Mills theory in the presence of a supersymmetric Wilson line. With a very simple computation, we are able to reproduce and extend existing results. We also propose a second relation, which reconstructs the correlator from a double discontinuity, and is controlled by the defect channel OPE. Finally, for the case of codimension-one defects (boundaries and interfaces) we derive a dispersion relation which receives contributions from both OPE channels and we apply it to the boundary correlator in the O(N)O(N) critical model. We reproduce the order ϵ2\epsilon^2 result in the ϵ\epsilon-expansion using as input a finite number of boundary CFT data.Comment: 36 pages, 3 figure

    Reputation Systems: A framework for attacks and frauds classification

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    Reputation and recommending systems have been widely used in e-commerce, as well as online collaborative networks, P2P networks and many other contexts, in order to provide trust to the participants involved in the online interaction. Based on a reputation score, the e-commerce user feels a sense of security, leading the person to trust or not when buying or selling. However, these systems may give the user a false sense of security due to their gaps. This article discusses the limitations of the current reputation systems in terms of models to determine the reputation score of the users. We intend to contribute to the knowledge in this field by providing a systematic overview of the main types of attack and fraud found in those systems, proposing a novel framework of classification based on a matrix of attributes. We believe such a framework could help analyse new types of attacks and fraud. Our work was based on a systematic literature review methodology.info:eu-repo/semantics/publishedVersio

    Ethnographies of Collaborative Economies across Europe: Understanding Sharing and Caring

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    "Sharing economy" and "collaborative economy" refer to a proliferation of initiatives, business models, digital platforms and forms of work that characterise contemporary life: from community-led initiatives and activist campaigns, to the impact of global sharing platforms in contexts such as network hospitality, transportation, etc. Sharing the common lens of ethnographic methods, this book presents in-depth examinations of collaborative economy phenomena. The book combines qualitative research and ethnographic methodology with a range of different collaborative economy case studies and topics across Europe. It uniquely offers a truly interdisciplinary approach. It emerges from a unique, long-term, multinational, cross-European collaboration between researchers from various disciplines (e.g., sociology, anthropology, geography, business studies, law, computing, information systems), career stages, and epistemological backgrounds, brought together by a shared research interest in the collaborative economy. This book is a further contribution to the in-depth qualitative understanding of the complexities of the collaborative economy phenomenon. These rich accounts contribute to the painting of a complex landscape that spans several countries and regions, and diverse political, cultural, and organisational backdrops. This book also offers important reflections on the role of ethnographic researchers, and on their stance and outlook, that are of paramount interest across the disciplines involved in collaborative economy research
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