Related papers: Federated Computing as Code (FCaC): Sovereignty-aw…
Coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has…
We propose embedding executable code fragments in cryptographically protected capabilities to enable flexible discretionary access control in cloud-like computing infrastructures. We are developing this as part of a sports analytics…
As the cloud computing paradigm has gained prominence, the need for verifiable computation has grown increasingly urgent. The concept of verifiable computation enables a weak client to outsource difficult computations to a powerful, but…
A model of cloud services is emerging whereby a few trusted providers manage the underlying hardware and communications whereas many companies build on this infrastructure to offer higher level, cloud-hosted PaaS services and/or SaaS…
Formal Concept Analysis (FCA) is extensively used in knowledge extraction, cognitive concept learning, and data mining. However, its computational demands on large-scale datasets often require outsourcing to external computing services,…
In cloud computing, data processing is delegated to a remote party for efficiency and flexibility reasons. A practical user requirement usually is that the confidentiality and integrity of data processing needs to be protected. In the…
Data privacy and silos are nontrivial and greatly challenging in many real-world applications. Federated learning is a decentralized approach to training models across multiple local clients without the exchange of raw data from client…
Context:Infrastructure as code (IaC) is the practice to automatically configure system dependencies and to provision local and remote instances. Practitioners consider IaC as a fundamental pillar to implement DevOps practices, which helps…
The paper illustrates how we built a federated cloud computing platform dedicated to the Italian research community. Building a cloud platform is a daunting task, that requires coordinating the deployment of many services, interrelated and…
Confidential computing plays an important role in isolating sensitive applications from the vast amount of untrusted code commonly found in the modern cloud. We argue that it can also be leveraged to build safer and more secure…
Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties.…
The digital identity problem is a complex one in large part because it involves personal data, the algorithms which compute reputations on the data and the management of the identifiers that are linked to personal data. The reality of today…
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might use the model parameters to derive sensitive information about…
OpenACC is a high-level directive-based parallel programming model that can manage the sophistication of heterogeneity in architectures and abstract it from the users. The portability of the model across CPUs and accelerators has gained the…
SAFE is a data-centric platform for building multi-domain networked systems, i.e., systems whose participants are controlled by different principals. Participants make trust decisions by issuing local queries over logic content exchanged in…
Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. By shifting the load of cloud computing to individual local servers, MEC…
Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge…
Scientific data management is at a critical juncture, driven by exponential data growth, increasing cross-domain dependencies, and a severe reproducibility crisis in modern research. Traditional centralized data management approaches are…
Federated learning is an emerging framework that builds centralized machine learning models with training data distributed across multiple devices. Most of the previous works about federated learning focus on the privacy protection and…
Federated identity management enables users to access multiple systems using a single login credential. However, to achieve this a complex privacy compromising authentication has to occur between the user, relying party (RP) (e.g., a…