Scaling Private Collaborated Consortium Blockchains Using State Machine Replication Over Random Graphs

Blockchain technology has redefined the way the software industry\u27s core mechanisms operate. With recent generations of improvement observed in blockchain, the industry is surging ahead towards replacing the existing computing paradigms with consortium blockchain-enabled solutions. For this, there is much research observed which aims to make blockchain technology’s performance at par with existing systems. Most of the research involves the optimization of the consensus algorithms that govern the system. One of the major aspects of upcoming iterations in blockchain technology is making individual consortium blockchains collaborate with other consortium blockchains to validate operations on a common set of data shared among the systems. The traditional approach involves requiring all the organizations to run the consensus and validate the change. This approach is computationally expensive and reduces the modularity of the system. Also, the optimized consensus algorithms have their specific requirements and assumptions which if extended to all the organizations leads to a cluttered system with high magnitudes of dependencies.This thesis proposes an architecture that leverages the use of state machine replication extended to all the nodes of different organizations with seamless updates over a random graph network without involving all the nodes participating in the consensus. This also enables organizations to run their respective consensus algorithms depending on their requirements. This approach guarantees the finality of consistent data updates with reduced computations with high magnitudes of scalability and flexibility

Towards federated learning over large-scale streaming data

2020 Spring.Includes bibliographical references.Distributed Stream Processing Engines (DSPEs) have seen significant deployment growth along with an increase in streaming data sources such as sensor networks. These DSPEs enable processing large amounts of streaming data in a cluster of commodity machines to extract knowledge and insights in real-time. Due to fluctuating data arrival rates in real-world applications, modern DSPEs often provide auto-scaling. However, the existing designs of advanced analytical frameworks are not effectively aligned with scalable streaming computing environments. We have designed and developed ORCA, a federated learning architecture that supports the training of traditional Artificial Neural Networks as well as Convolutional Neural Networks and Long Short-term Memory Network based models while ensuring resiliency during scaling. ORCA also introduces dynamic adjustment of the 'elasticity' hyper-parameter for rescaled computing environments. We estimate this elasticity hyper-parameter using reinforcement learning. Our empirical benchmarks show that ORCA is capable of achieving an MSE of 0.038 over real-world streaming datasets

A scalable system for factored learning in the cloud

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 79-81).This work presents FlexGP, a new system designed for scalable machine learning in the cloud. FlexGP presents a learner-agnostic, data-parallel approach to cloud-based distributed learning using existing single-machine algorithms, without any dependence on distributed file systems or shared memory between instances. We design and implement asynchronous and decentralized launch and peer discovery protocols to start and configure a distributed network of learners. Through a unique process of factoring the data and parameters across the learners, FlexGP ensures this network consists of heterogeneous learners producing diverse models. These models are then filtered and fused to produce a meta-model for prediction. Using a thoughtfully designed test framework, FlexGP is run on a real-world regression problem from a large database. The results demonstrate the reliability and robustness of the system, even when learning from very little training data and multiple factorings, and demonstrate FlexGP as a vital tool to effectively leverage the cloud for machine learning tasks.by Owen C. Derby.M. Eng

Robust health stream processing

2014 Fall.Includes bibliographical references.As the cost of personal health sensors decrease along with improvements in battery life and connectivity, it becomes more feasible to allow patients to leave full-time care environments sooner. Such devices could lead to greater independence for the elderly, as well as for others who would normally require full-time care. It would also allow surgery patients to spend less time in the hospital, both pre- and post-operation, as all data could be gathered via remote sensors in the patients home. While sensor technology is rapidly approaching the point where this is a feasible option, we still lack in processing frameworks which would make such a leap not only feasible but safe. This work focuses on developing a framework which is robust to both failures of processing elements as well as interference from other computations processing health sensor data. We work with 3 disparate data streams and accompanying computations: electroencephalogram (EEG) data gathered for a brain-computer interface (BCI) application, electrocardiogram (ECG) data gathered for arrhythmia detection, and thorax data gathered from monitoring patient sleep status

Route Discovery in Private Payment Channel Networks

In this work, we are the first to explore route discovery in private channel networks. We first determine what ``ideal privacy for a routing protocol means in this setting. We observe that protocols achieving this strong privacy definition exist by leveraging (topology hiding) Multi-Party Computation but they are (inherently) inefficient as route discovery must involve the entire network. We then present protocols with weaker privacy guarantees but much better efficiency. In particular, route discovery typically only involves small fraction of the nodes but some information on the topology and balances -- beyond what is necessary for performing the transaction -- is leaked. The core idea is that both sender and receiver gossip a message which then slowly propagates through the network, and the moment any node in the network receives both messages, a path is found. In our first protocol the message is always sent to all neighbouring nodes with a delay proportional to the fees of that edge. In our second protocol the message is only sent to one neighbour chosen randomly with a probability proportional to its degree. While the first instantiation always finds the cheapest path, the second might not, but it involves a smaller fraction of the network. % We discuss some extensions like employing bilinear maps so the gossiped messages can be re-randomized, making them unlikeable and thus improving privacy. We also discuss some extensions to further improve privacy by employing bilinear maps. Simulations of our protocols on the Lightning network topology (for random transactions and uniform fees) show that our first protocol (which finds the cheapest path) typically involves around 12\% of the 6376 nodes, while the second only touches around 18 nodes $(<0.3\%)$, and the cost of the path that is found is around twice the cost of the optimal one

Global connectivity architecture of mobile personal devices

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 193-207).The Internet's architecture, designed in the days of large, stationary computers tended by technically savvy and accountable administrators, fails to meet the demands of the emerging ubiquitous computing era. Nontechnical users now routinely own multiple personal devices, many of them mobile, and need to share information securely among them using interactive, delay-sensitive applications.Unmanaged Internet Architecture (UIA) is a novel, incrementally deployable network architecture for modern personal devices, which reconsiders three architectural cornerstones: naming, routing, and transport. UIA augments the Internet's global name system with a personal name system, enabling users to build personal administrative groups easily and intuitively, to establish secure bindings between his devices and with other users' devices, and to name his devices and his friends much like using a cell phone's address book. To connect personal devices reliably, even while mobile, behind NATs or firewalls, or connected via isolated ad hoc networks, UIA gives each device a persistent, location-independent identity, and builds an overlay routing service atop IP to resolve and route among these identities. Finally, to support today's interactive applications built using concurrent transactions and delay-sensitive media streams, UIA introduces a new structured stream transport abstraction, which solves the efficiency and responsiveness problems of TCP streams and the functionality limitations of UDP datagrams. Preliminary protocol designs and implementations demonstrate UIA's features and benefits. A personal naming prototype supports easy and portable group management, allowing use of personal names alongside global names in unmodified Internet applications. A prototype overlay router leverages the naming layer's social network to provide efficient ad hoc connectivity in restricted but important common-case scenarios.(cont) Simulations of more general routing protocols--one inspired by distributed hash tables, one based on recent compact routing theory--explore promising generalizations to UIA's overlay routing. A library-based prototype of UIA's structured stream transport enables incremental deployment in either OS infrastructure or applications, and demonstrates the responsiveness benefits of the new transport abstraction via dynamic prioritization of interactive web downloads. Finally, an exposition and experimental evaluation of NAT traversal techniques provides insight into routing optimizations useful in UIA and elsewhere.by Bryan Alexander Ford.Ph.D

Security Threats Classification in Blockchains

Blockchain, the foundation of Bitcoin, has become one of the most popular technologies to create and manage digital transactions recently. It serves as an immutable ledger which allows transactions take place in a decentralized manner. This expeditiously evolving technology has the potential to lead to a shift in thinking about digital transactions in multiple sectors including, Internet of Things, healthcare, energy, supply chain, manufacturing, cybersecurity and principally financial services. However, this emerging technology is still in its infancy. Despite the huge opportunities blockchain offers, it suffers from challenges and limitation such as scalability, security, and privacy, compliance, and governance issues that have not yet been thoroughly explored and addressed. Although there are some studies on the security and privacy issues of the blockchain, they lack a systematic examination of the security of blockchain systems. This research conducted a systematic survey of the security threats to the blockchain systems and reviewed the existing vulnerabilities in the Blockchain. These vulnerabilities lead to the execution of the various security threats to the normal functionality of the Blockchain platforms. Moreover, the study provides a case-study for each attack by examining the popular blockchain systems and also reviews possible countermeasures which could be used in the development of various blockchain systems. Furthermore, this study developed taxonomies that classified the security threats and attacks based on the blockchain abstract layers, blockchain primary processes and primary business users. This would assist the developers and businesses to be attentive to the existing threats in different areas of the blockchain-based platforms and plan accordingly to mitigate risk. Finally, summarized the critical open challenges, and suggest future research directions

Improving Vehicular ad hoc Network Protocols to Support Safety Applications in Realistic Scenarios

La convergencia de las telecomunicaciones, la informática, la tecnología inalámbrica y los sistemas de transporte, va a facilitar que nuestras carreteras y autopistas nos sirvan tanto como plataforma de transporte, como de comunicaciones. Estos cambios van a revolucionar completamente cómo y cuándo vamos a acceder a determinados servicios, comunicarnos, viajar, entretenernos, y navegar, en un futuro muy cercano. Las redes vehiculares ad hoc (vehicular ad hoc networks VANETs) son redes de comunicación inalámbricas que no requieren de ningún tipo de infraestructura, y que permiten la comunicación y conducción cooperativa entre los vehículos en la carretera. Los vehículos actúan como nodos de comunicación y transmisores, formando redes dinámicas junto a otros vehículos cercanos en entornos urbanos y autopistas. Las características especiales de las redes vehiculares favorecen el desarrollo de servicios y aplicaciones atractivas y desafiantes. En esta tesis nos centramos en las aplicaciones relacionadas con la seguridad. Específicamente, desarrollamos y evaluamos un novedoso protocol que mejora la seguridad en las carreteras. Nuestra propuesta combina el uso de información de la localización de los vehículos y las características del mapa del escenario, para mejorar la diseminación de los mensajes de alerta. En las aplicaciones de seguridad para redes vehiculares, nuestra propuesta permite reducir el problema de las tormentas de difusión, mientras que se mantiene una alta efectividad en la diseminación de los mensajes hacia los vehículos cercanos. Debido a que desplegar y evaluar redes VANET supone un gran coste y una tarea dura, la metodología basada en la simulación se muestra como una metodología alternativa a la implementación real. A diferencia de otros trabajos previos, con el fin de evaluar nuestra propuesta en un entorno realista, en nuestras simulaciones tenemos muy en cuenta tanto la movilidad de los vehículos, como la transmisión de radio en entornos urbanos, especialmente cuando los edificios interfieren en la propagación de la señal de radio. Con este propósito, desarrollamos herramientas para la simulación de VANETs más precisas y realistas, mejorando tanto la modelización de la propagación de radio, como la movilidad de los vehículos, obteniendo una solución que permite integrar mapas reales en el entorno de simulación. Finalmente, evaluamos las prestaciones de nuestro protocolo propuesto haciendo uso de nuestra plataforma de simulación mejorada, evidenciando la importancia del uso de un entorno de simulación adecuado para conseguir resultados más realistas y poder obtener conclusiones más significativas.Martínez Domínguez, FJ. (2010). Improving Vehicular ad hoc Network Protocols to Support Safety Applications in Realistic Scenarios [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9195Palanci