No Arabic abstract
Recent works have proposed new Byzantine consensus algorithms for blockchains based on epidemics, a design which enables highly scalable performance at a low cost. These methods however critically depend on a secure random peer sampling service: a service that provides a stream of random network nodes where no attacking entity can become over-represented. To ensure this security property, current epidemic platforms use a Proof-of-Stake system to select peer samples. However such a system limits the openness of the system as only nodes with significant stake can participate in the consensus, leading to an oligopoly situation. Moreover, this design introduces a complex interdependency between the consensus algorithm and the cryptocurrency built upon it. In this paper, we propose a radically different security design for the peer sampling service, based on the distribution of IP addresses to prevent Sybil attacks. We propose a new algorithm, $scriptstyle{BASALT}$, that implements our design using a stubborn chaotic search to counter attackers attempts at becoming over-represented. We show in theory and using Monte Carlo simulations that $scriptstyle{BASALT}$ provides samples which are extremely close to the optimal distribution even in adversarial scenarios such as tentative Eclipse attacks. Live experiments on a production cryptocurrency platform confirm that the samples obtained using $scriptstyle{BASALT}$ are equitably distributed amongst nodes, allowing for a system which is both open and where no single entity can gain excessive power.
In the field of database deduplication, the goal is to find approximately matching records within a database. Blocking is a typical stage in this process that involves cheaply finding candidate pairs of records that are potential matches for further processing. We present here Hashed Dynamic Blocking, a new approach to blocking designed to address datasets larger than those studied in most prior work. Hashed Dynamic Blocking (HDB) extends Dynamic Blocking, which leverages the insight that rare matching values and rare intersections of values are predictive of a matching relationship. We also present a novel use of Locality Sensitive Hashing (LSH) to build blocking key values for huge databases with a convenient configuration to control the trade-off between precision and recall. HDB achieves massive scale by minimizing data movement, using compact block representation, and greedily pruning ineffective candidate blocks using a Count-min Sketch approximate counting data structure. We benchmark the algorithm by focusing on real-world datasets in excess of one million rows, demonstrating that the algorithm displays linear time complexity scaling in this range. Furthermore, we execute HDB on a 530 million row industrial dataset, detecting 68 billion candidate pairs in less than three hours at a cost of $307 on a major cloud service.
Blockchain-based cryptocurrencies received a lot of attention recently for their applications in many domains. IoT domain is one of such applications, which can utilize cryptocur-rencies for micro payments without compromising their payment privacy. However, long confirmation times of transactions and relatively high fees hinder the adoption of cryptoccurency based micro-payments. The payment channel networks is one of the proposed solutions to address these issue where nodes establish payment channels among themselves without writing on blockchain. IoT devices can benefit from such payment networks as long as they are capable of sustaining their overhead. Payment channel networks pose unique characteristics as far as the routing problem is concerned. Specifically, they should stay balanced to have a sustainable network for maintaining payments for longer times, which is crucial for IoT devices once they are deployed.In this paper, we present a payment channel network design that aims to keep the channels balanced by using a common weight policy across the network. We additionally propose using multi-point connections to nodes for each IoT device for unbalanced payment scenarios. The experiment results show that we can keep the channels in the network more equally balanced compared to the minimal fee approach. In addition, multiple connections from IoT devices to nodes increase the success ratio significantly.
Radio Access Networks (RAN) tends to be more distributed in the 5G and beyond, in order to provide low latency and flexible on-demanding services. In this paper, Blockchain-enabled Radio Access Networks (BE-RAN) is proposed as a novel decentralized RAN architecture to facilitate enhanced security and privacy on identification and authentication. It can offer user-centric identity management for User Equipment (UE) and RAN elements, and enable mutual authentication to all entities while enabling on-demand point-to-point communication with accountable billing service add-on on public network. Also, a potential operating model with thorough decentralization of RAN is envisioned. The paper also proposed a distributed privacy-preserving P2P communication approach, as one of the core use cases for future mobile networks, is presented as an essential complement to the existing core network-based security and privacy management. The results show that BE-RAN significantly improves communication and computation overheads compared to the existing communication authentication protocols.
Epidemic situations typically demand intensive data collection and management from different locations/entities within a strict time constraint. Such demand can be fulfilled by leveraging the intensive and easy deployment of the Internet of Things (IoT) devices. The management and containment of such situations also rely on cross-organizational and national collaboration. Thus, this paper proposes an Intelligent-Health (I-Health) system that aims to aggregate diverse e-health entities in a unique national healthcare system by enabling swift, secure exchange and storage of medical data. In particular, we design an automated patients monitoring scheme, at the edge, which enables the prompt discovery, remote monitoring, and fast emergency response for critical medical events, such as emerging epidemics. Furthermore, we develop a blockchain optimization model that aims to optimize medical data sharing between different health entities to provide effective and secure health services. Finally, we show the effectiveness of our system, in adapting to different critical events, while highlighting the benefits of the proposed I-Health system.
In this proceeding, we summarize the key science goals and reference design for a next-generation Very Large Array (ngVLA) that is envisaged to operate in the 2030s. The ngVLA is an interferometric array with more than 10 times the sensitivity and spatial resolution of the current VLA and ALMA, that will operate at frequencies spanning $sim 1.2 -116$ GHz, thus lending itself to be highly complementary to ALMA and the SKA1. As such, the ngVLA will tackle a broad range of outstanding questions in modern astronomy by simultaneously delivering the capability to: unveil the formation of Solar System analogues; probe the initial conditions for planetary systems and life with astrochemistry; characterize the assembly, structure, and evolution of galaxies from the first billion years to the present; use pulsars in the Galactic center as fundamental tests of gravity; and understand the formation and evolution of stellar and supermassive blackholes in the era of multi-messenger astronomy.