A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. This paper aims to fill this research gap by undertaking a systematic literature review on the use of quantization in distributed learning for privacy enhancement. We explore peer-reviewed literature that utilizes quantization for privacy-preserving purposes. Our analysis identifies the limitations and challenges of current approaches. It also highlights the need to integrate quantization techniques for dual objectives (privacy and communication efficiency) in distributed learning frameworks.

Internet of Things (IoT) is composed of physical devices, communication networks, and services provided by edge systems and over-the-top applications. IoT connects billions of devices that collect data from the physical environment, which are pre-processed at the edge and then forwarded to processing services at the core of the infrastructure, on top of which cloud-based applications are built and provided to mobile end users. IoT comes with important advantages in terms of applications and added value for its users, making their world smarter and simpler. These advantages, however, are mitigated by the difficulty of guaranteeing IoT trustworthiness, which is still in its infancy. IoT trustworthiness is a must especially in critical domains (e.g., health, transportation) where humans become new components of an IoT system and their life is put at risk by system malfunctioning or breaches. In this article, we put forward the idea that trust in IoT can be boosted if and only if its automation and adaptation processes are based on trustworthy data. We therefore depart from a scenario that considers the quality of a single decision as the main goal of an IoT system and consider the trustworthiness of collected data as a fundamental requirement at the basis of a trustworthy IoT environment. We therefore define a methodology for data collection that filters untrusted data out according to trust rules evaluating the status of the devices collecting data and the collected data themselves. Our approach is based on blockchain and smart contracts and collects data whose trustworthiness and integrity are proven over time. The methodology balances trustworthiness and privacy and is experimentally evaluated in real-world and simulated scenarios using Hyperledger fabric blockchain.

Performance and dependability levels of cloud-based computations are difficult to guarantee by-design due to segregation of visibility and control between applications, data owners, and cloud providers. Lack of predictability increases users' uncertainty about the service levels they will actually achieve. Cloud tenants compete for shared resources/services at all layers of the cloud stack, and pose heterogeneous and conflicting non-functional requirements over them. These requirements have implications for platform and infrastructure layers, which have to be configured to satisfy inter-tenants requirements. We argue that adaptation techniques can play a crucial role in providing a reliable cloud, supporting definite behavior of applications and stable quality of service. Existing adaptation techniques however are unsuitable for cloud use, since they mostly focus on single tenancy, performance requirements, and are based on unverifiable evidence, which is collected in an untrusted way. In this paper, we propose a multi-tenant, general-purpose adaptation technique for the cloud, based on evidence collected by means of a trustworthy certification process. We depart from traditional heavy and comprehensive certification processes, such as ISO/IEC 27017, and consider a flexible and lightweight certification process for the cloud. It is based on authentic evidence and provides accountable validation on the compliance of a cloud-based system. Our approach adapts the cloud at all layers to maintain stable non-functional properties in certificates over time, by continuously verifying certificate validity. We assess the performance and quality of our adaptation approach in a wide range of settings.

This paper introduces a framework for Data Protection as a Service (DPaaS) to cloud computing users. Compared to the existing Data Encryption as a Service (DEaaS) such as those provided by Amazon and Google, our DPaaS framework provides more flexibility, control and visibility for protecting data in the cloud. In addition to supporting the basic data encryption capability as DEaaS does, this DPaaS framework allows data owners to define fine-grained access control policies to protect their data. Data protected by an access control policy are automatically encrypted and access is granted to user/applications according with the policy. In general, the DPaaS enables the separation of concerns between security and data management, in addition to defining a full cycle of data security automation from encryption to decryption. Our proof-of-concept prototype of the DPaaS works with hybrid multi-cloud environments including private clouds and virtual data-centers using OpenStack, CloudStack and VMWare as well as public clouds being the BT Cloud Compute platform and Amazon (AWS). Experiments on the prototype have proved the efficiency of the framework.

Opportunities arising from IoT-enabled applications are significant, but market growth is inhibited by concerns over security and complexity. To address these issues, we propose the ERAMIS methodology, which is based on instantiation of a reference architecture that captures common design features, embodies best practice, incorporates good security properties by design, and makes explicit provision for operational security services and processes.

Cloud computing based Data-as-a-Service (DaaS) has become popular. Several data assets have been released in DaaSes across different cloud platforms. Nevertheless, there are no well-defined ways to describe DaaSes and their associated data assets. On the one hand, existing DaaS providers simply use HTML documents to describe their service. This simple way of service description requires user to manually perform service lookup by reading the HTML documents to understand DaaSes as well as their provided data assets. On the other hand, existing service description techniques are not suitable for describing DaaSes because they consider only service information. The lack of well-structured/linked model to describe DaaSes hinders the automatic service lookup for DaaSes and the integration of DaaSes into data composition and analytic tools. In this paper, we propose DEMODS, a Description Model for DaaS, which introduces a general linked model to cover all basic information of a DaaS. Besides the basic DaaS description model, we also introduce an extended model that integrates existing work in describing quality of data, data and service contract, data dependency, and Quality of Service (QoS). We present a mechanism to incorporate DEMODS into both new and existing DaaSes. Finally, a prototype of DEMODS has been developed to evaluate the effectiveness of the proposed model.

Peer-to-Peer (P2P) systems are generally known as open and free systems since resources are often contributed from voluntary participants. Their limitation, however, is that they only support simple resource sharing such as file (storage) sharing and bandwidth sharing. On the other hand, based on virtualization techniques, cloud computing provides a more flexible mechanism to share all types of resources with no limitations. Nevertheless, the use of cloud computing usually requires a fee and is often locked-in to services of cloud computing providers (i.e., vendor lock-in). In this paper, we aim to combine P2P and cloud computing technologies to design and develop PC2, an open and free cloud computing platform that allows users to participate in any time to contribute as well as to consume sharing resources. While this combination inherits good advantages of these two technologies, it also avoid their disadvantages. As a proof-of-concept, we have developed a prototype for PC2based on Eucalyptus, the most popular open source for building cloud computing platforms.

Cloud computing technology has become popular of late due to several advantages it has compared to traditional computing models. For example, cloud computing helps users to save a significant amount of cost that is related to infrastructure investment, management, and maintenance. Furthermore, since cloud computing offers agility, flexibility, and scalability, applications could be deployed, developed, and scaled quickly and easily on the cloud. Nevertheless, while it is not difficult to develop a new application on the cloud, there is a question of whether legacy applications could be moved to the cloud. Knowing the answer for this question is very important to users, who have stable applications in traditional computing platforms, but want to move these applications to the cloud to leverage the advantages of cloud computing. In this paper, we will analyze features of cloud computing services to introduce an answer for this question. In particular, our answer will focus on the practicability and methodology of migrating legacy applications to the cloud. As a proof-of-concept, we support our answer with the migration of three existing applications being used at British Telecom to Google Apps Engine: one is a Java-based application while the two others are Python-based applications. Among the two Python-based applications, one requires a migration of its database in addition to the migration of the application itself.