The micro cloud model offers cloud behavior at the edge of the network. It allows dynamic organization of the resources, closer to the users and the data. One of the crucial problems to solve is to design a proper cloud model at the edge of the network and offer cloud services that support multi-tenancy. This cloud property is a governing mechanism to lower cloud costs. It is essential for the scalability of both public and private clouds due to the utilization of shared resources and the logical separation of tenants. This paper presents the model for the creation of virtual clouds (vClouds) on physical infrastructure using a hierarchy of namespaces, with proper organization and redistribution of resources such as CPU, RAM, and storage, while preserving logical isolation between vClouds, thus creating a multi-tenant system. The presented model guarantees accurate resource redistribution through graph transformations to model operations, while the proposed protocols ensure correctness by employing multiparty session types for modeling. We extend the secure computing mode to establish an isolated system, allowing sandboxing rules for every namespace and creating hierarchies of security profiles. This advancement enables our model to inherit parent security profiles fully, extend them by adding child-specific elements, or redefine and create entirely new ones. Furthermore, the users can switch context, meaning they can change vCloud or the namespace they operate on.

In modern architecture, multi-tenancy is the design mode of operation used to reduce the resource costs of system implementation. This architectural decision does not come without additional costs when managing isolation and data privacy between the tenants. That is a known challenge in cloud computing that translates to systems closer to the users, namely edge computing and micro clouds. Existing data management solutions, while supporting multi-tenancy, need help to handle the dynamic nature of such environments, limiting their ability to scale and support collaborative applications across different clusters or regions. This paper addresses the limitations of current data management solutions in supporting dynamic organization, configuration, and collaboration in multi-tenant, geo-distributed micro clouds at the edge. By extending the existing virtual clouds model, we introduce a new dataspaces layer and protocols for data discovery using the concept of hierarchical namespaces and transforming non-shareable resources, such as files and folders, into collaborative resources. We propose two types of collaborative applications, leveraging stored procedures and event triggers for data consumption. Finally, we ensure that the model provides accurate access and redistribution of shareable (CPU, RAM, disk) and non-shareable resources through graph transformation theory, with correctness validated using multiparty session types. That enables more flexible programming for collaborative applications, improving connectivity across distributed storage systems and allowing resource borrowing between applications within the same organization. This research seeks to bridge the gap between current tools and the demands of modern distributed systems, enabling more robust, adaptable, and collaborative data management solutions.

Deep Learning as a Service (DLaaS) has become a cornerstone in enabling access to deep learning capabilities, allowing users to train models or leverage pre-trained ones through APIs. This paradigm significantly lowers the barrier to entry for deploying complex AI systems, making cutting-edge technologies accessible to a broader audience. However, the growing reliance on DLaaS poses significant privacy concerns, mainly when sensitive data is involved. Addressing these challenges necessitates Privacy-Preserving Deep Learning (PPDL), an emerging field focused on safeguarding data and model privacy during training and inference without compromising utility or performance. This paper comprehensively reviews the current PPDL landscape from theoretical, software, and hardware perspectives. We analyze over 100 recent works in areas such as Homomorphic Encryption, Functional Encryption, Multi-Party Computation, Trusted Execution Environments, Federated Learning, and Differential Privacy. We detail core methodologies, technologies, and their applications, comparing approaches to high-light strengths and weaknesses.Visual summaries of key contributions are presented to aid understanding and provide an accessible overview of the field. Furthermore, we discuss the limitations of existing techniques and identify several future research directions.

The critical infrastructure is constantly under cyber and physical threats. Applying security controls without guidance or traceability can create a false sense of security. Security standards facilitate security knowledge and control best practices in a more systematic way. However, the number of standards is continually increasing. Product providers that operate in multiple geographical regions often face the obligation to comply with multiple standards simultaneously. This introduces the problem of the convenient interpretation of different standards. Thus, a comprehensive analysis of the requirements from different security standards and guidelines applicable to the smart grid has been performed to detect similarities that can be shaped into entities of the conceptual model for requirement representation. The purpose of the model—presented in a form of a Unified Modeling Language (UML) class diagram—is to give product providers a canonical way to map requirements from arbitrary standards, guidelines, and regulations and accelerate the cross-standard compliance readiness by defining priority for requirement implementation. In addition, the research showed that multiple vectors should impact the priority of the implementation of the security controls defined through the requirements: domain affiliation, the essence of the requirement, associated threats, risks, and social dependencies between actors involved in the implementation. To examine the model correctness, NISTIR 7628—de facto smart grid standard—was used to provide insights into how the model would be used for requirements implementation tracking. The structure of individual requirements was analyzed to detect the building blocks and extract relevant parts that can be mapped to the model components. Further, all requirements were classified into one of the defined domains to provide the basis for referencing similar requirements from different standards. Finally, one arbitrary requirement was used to demonstrate model usage, and depict all available information that can be provided to the users in a custom-made scenario where the need arises to have simultaneous alignment with three standards—NISTIR 7628, NIST 800-53, and IEC 62443-3-3.