2,030 research outputs found
PLACES'10: The 3rd Workshop on Programmng Language Approaches to concurrency and Communication-Centric Software
Paphos, Cyprus. March 201
Catchment Care - Developing an Auction Process for Biodiversity and Water Quality Gains. Volume 1 - Report
This report describes the design, development and trial of catchment care. Catchment Care is an auction-based system which aims to increase the cost effectiveness of funds for private on-ground natural resource management actions.Water;Australia;Natural Resource Management;Catchment Care; auction.
Resource Management in Large-scale Systems
The focus of this thesis is resource management in large-scale systems. Our primary concerns are energy management and practical principles for self-organization and self-management. The main contributions of our work are: 1. Models. We proposed several models for different aspects of resource management, e.g., energy-aware load balancing and application scaling for the cloud ecosystem, hierarchical architecture model for self-organizing and self-manageable systems and a new cloud delivery model based on auction-driven self-organization approach. 2. Algorithms. We also proposed several different algorithms for the models described above. Algorithms such as coalition formation, combinatorial auctions and clustering algorithm for scale-free organizations of scale-free networks. 3. Evaluation. Eventually we conducted different evaluations for the proposed models and algorithms in order to verify them. All the simulations reported in this thesis had been carried out on different instances and services of Amazon Web Services (AWS). All of these modules will be discussed in detail in the following chapters respectively
ARPA Whitepaper
We propose a secure computation solution for blockchain networks. The
correctness of computation is verifiable even under malicious majority
condition using information-theoretic Message Authentication Code (MAC), and
the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty
computation protocol and a layer2 solution, our privacy-preserving computation
guarantees data security on blockchain, cryptographically, while reducing the
heavy-lifting computation job to a few nodes. This breakthrough has several
implications on the future of decentralized networks. First, secure computation
can be used to support Private Smart Contracts, where consensus is reached
without exposing the information in the public contract. Second, it enables
data to be shared and used in trustless network, without disclosing the raw
data during data-at-use, where data ownership and data usage is safely
separated. Last but not least, computation and verification processes are
separated, which can be perceived as computational sharding, this effectively
makes the transaction processing speed linear to the number of participating
nodes. Our objective is to deploy our secure computation network as an layer2
solution to any blockchain system. Smart Contracts\cite{smartcontract} will be
used as bridge to link the blockchain and computation networks. Additionally,
they will be used as verifier to ensure that outsourced computation is
completed correctly. In order to achieve this, we first develop a general MPC
network with advanced features, such as: 1) Secure Computation, 2) Off-chain
Computation, 3) Verifiable Computation, and 4)Support dApps' needs like
privacy-preserving data exchange
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
Recommended from our members
Improving shared access to Cloud of Things resources.
Cloud of Things (CoT) is an emerging paradigm that integrates Cloud Computing and Internet of Things (IoT) to support a wide range of real-world applications. Resource allocation plays a vital role in CoT, especially when allocating IoT physical resources to Cloud-based applications to ensure seamless application execution. Due to the heterogeneity and the constrained capacities of IoT resources, resource allocation is a challenge. This complexity leads to missing/limiting shared access to the IoT physical resources and consequently lessen the reusability of the resources across multiple applications. This issue results in, 1) replicating IoT deployments making them expensive and not feasible for many prospective users, 2) existing IoT infrastructures are over-provisioned to meet the unpredictable application requirements in which resources may be signiïŹcantly underutilised, and 3) the adoption of CoT is slowed.
Improving shared access to CoT resources can provide eïŹcient resource allocation, improve resource utilisation and likely to reduce the cost of IoT deployments. Existing solutions include small-scale, hardware and platform-dependent mechanisms to enable or improve shared access to IoT resources. The research presented in this thesis considers trading CoT resources in a marketplace as an approach to improve shared access to CoT resources. It proposes a solution to Cot resource allocation that re-imagines CoT resources as commodities that can be provided and consumed by the marketplace participants.
The novel contributions of the research presented in this thesis are summarised as follows: 1) a model to describe and quantify the value of CoT resources, 2) a resource sharing and allocation strategy called Exclusive Shared Access (ESA) to CoT resources, 3) a QoS-aware optimisation model for trading CoT resources as a single and multipleobjective optimisation problem, and 4) a marketplace architecture and experimental evaluation to verify its performance and scalability
Security in Cloud Computing: Evaluation and Integration
Au cours de la derniĂšre dĂ©cennie, le paradigme du Cloud Computing a rĂ©volutionnĂ© la maniĂšre dont nous percevons les services de la Technologie de lâInformation (TI). Celui-ci nous a donnĂ© lâopportunitĂ© de rĂ©pondre Ă la demande constamment croissante liĂ©e aux besoins informatiques
des usagers en introduisant la notion dâexternalisation des services et des donnĂ©es. Les consommateurs du Cloud ont gĂ©nĂ©ralement accĂšs, sur demande, Ă un large Ă©ventail bien
rĂ©parti dâinfrastructures de TI offrant une plĂ©thore de services. Ils sont Ă mĂȘme de configurer dynamiquement les ressources du Cloud en fonction des exigences de leurs applications, sans toutefois devenir partie intĂ©grante de lâinfrastructure du Cloud. Cela leur permet dâatteindre
un degrĂ© optimal dâutilisation des ressources tout en rĂ©duisant leurs coĂ»ts dâinvestissement en TI. Toutefois, la migration des services au Cloud intensifie malgrĂ© elle les menaces existantes Ă la sĂ©curitĂ© des TI et en crĂ©e de nouvelles qui sont intrinsĂšques Ă lâarchitecture du Cloud
Computing. Câest pourquoi il existe un rĂ©el besoin dâĂ©valuation des risques liĂ©s Ă la sĂ©curitĂ© du Cloud durant le procĂ©dĂ© de la sĂ©lection et du dĂ©ploiement des services. Au cours des derniĂšres annĂ©es, lâimpact dâune efficace gestion de la satisfaction des besoins en sĂ©curitĂ© des
services a Ă©tĂ© pris avec un sĂ©rieux croissant de la part des fournisseurs et des consommateurs. Toutefois, lâintĂ©gration rĂ©ussie de lâĂ©lĂ©ment de sĂ©curitĂ© dans les opĂ©rations de la gestion des ressources du Cloud ne requiert pas seulement une recherche mĂ©thodique, mais aussi une modĂ©lisation mĂ©ticuleuse des exigences du Cloud en termes de sĂ©curitĂ©.
Câest en considĂ©rant ces facteurs que nous adressons dans cette thĂšse les dĂ©fis liĂ©s Ă lâĂ©valuation de la sĂ©curitĂ© et Ă son intĂ©gration dans les environnements indĂ©pendants et interconnectĂ©s du Cloud Computing. Dâune part, nous sommes motivĂ©s Ă offrir aux consommateurs du Cloud un ensemble de mĂ©thodes qui leur permettront dâoptimiser la sĂ©curitĂ© de leurs services et, dâautre part, nous offrons aux fournisseurs un Ă©ventail de stratĂ©gies qui leur permettront de mieux sĂ©curiser leurs services dâhĂ©bergements du Cloud. LâoriginalitĂ© de cette thĂšse porte sur deux aspects : 1) la description innovatrice des exigences des applications du Cloud relativement Ă la sĂ©curitĂ© ; et 2) la conception de modĂšles mathĂ©matiques rigoureux qui intĂšgrent le facteur de sĂ©curitĂ© dans les problĂšmes traditionnels du dĂ©ploiement des applications, dâapprovisionnement des ressources et de la gestion de la charge de travail au coeur des infrastructures
actuelles du Cloud Computing. Le travail au sein de cette thÚse est réalisé en trois phases.----------ABSTRACT: Over the past decade, the Cloud Computing paradigm has revolutionized the way we envision IT services. It has provided an opportunity to respond to the ever increasing computing needs of the users by introducing the notion of service and data outsourcing. Cloud consumers usually
have online and on-demand access to a large and distributed IT infrastructure providing a plethora of services. They can dynamically configure and scale the Cloud resources according to the requirements of their applications without becoming part of the Cloud infrastructure, which allows them to reduce their IT investment cost and achieve optimal resource utilization. However, the migration of services to the Cloud increases the vulnerability to existing IT security threats and creates new ones that are intrinsic to the Cloud Computing architecture, thus the need for a thorough assessment of Cloud security risks during the process of service selection and deployment. Recently, the impact of effective management of service security satisfaction has been taken with greater seriousness by the Cloud Service Providers (CSP) and stakeholders. Nevertheless, the successful integration of the security element into the Cloud resource management operations does not only require methodical research, but also necessitates the meticulous modeling of the Cloud security requirements.
To this end, we address throughout this thesis the challenges to security evaluation and integration in independent and interconnected Cloud Computing environments. We are interested in providing the Cloud consumers with a set of methods that allow them to optimize the security of their services and the CSPs with a set of strategies that enable them to provide security-aware Cloud-based service hosting. The originality of this thesis lies within two aspects: 1) the innovative description of the Cloud applicationsâ security requirements, which paved the way for an effective quantification and evaluation of the security of Cloud infrastructures; and 2) the design of rigorous mathematical models that integrate the security factor into the traditional problems of application deployment, resource provisioning, and workload management within current Cloud Computing infrastructures. The work in this thesis is carried out in three phases
Participation and Data Valuation in IoT Data Markets through Distributed Coalitions
This paper considers a market for trading Internet of Things (IoT) data that
is used to train machine learning models. The data, either raw or processed, is
supplied to the market platform through a network and the price of such data is
controlled based on the value it brings to the machine learning model. We
explore the correlation property of data in a game-theoretical setting to
eventually derive a simplified distributed solution for a data trading
mechanism that emphasizes the mutual benefit of devices and the market. The key
proposal is an efficient algorithm for markets that jointly addresses the
challenges of availability and heterogeneity in participation, as well as the
transfer of trust and the economic value of data exchange in IoT networks. The
proposed approach establishes the data market by reinforcing collaboration
opportunities between device with correlated data to avoid information leakage.
Therein, we develop a network-wide optimization problem that maximizes the
social value of coalition among the IoT devices of similar data types; at the
same time, it minimizes the cost due to network externalities, i.e., the impact
of information leakage due to data correlation, as well as the opportunity
costs. Finally, we reveal the structure of the formulated problem as a
distributed coalition game and solve it following the simplified
split-and-merge algorithm. Simulation results show the efficacy of our proposed
mechanism design toward a trusted IoT data market, with up to 32.72% gain in
the average payoff for each seller.Comment: 14 pages. Submitted for possible publicatio
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