Machine-learning generative Artificial Intelligence tools, specifically large-language models, provide varied functionality, like content generation, user-facing chatbots, and code generation. The LLM typically works with a decision engine, such as a neural network. LLMs suffer issues with training data poisoning, copyright of generated content, and this paper's focus; prompt engineering attacks and training data leaks. The authors propose an architecture to co-locate a filtering mechanism with the LLM chatbot to identify and preventing disclosure of leaked LLM training data before communication to the end-user. Implementation of a resource description framework (RDF) based filtering mechanism compares LLM outputs against a bank of training data using three approaches; the first uses a bank of hash-codes generated from training data artifacts, the second uses a bank of training data stored as plaintext, and the third couples natural language processing (NLP) with the plaintext training data bank. Accuracy, overhead and acceleration results are detailed, and observed anomalies in LLM responses to testing including plausible leaks are also discussed.

This paper presents the implementation of a fully functional 5G network on a compact, energy-efficient edge micro datacenter, showcasing features such as workload prediction and preemptive scaling. The network extends across multiple edge micro datacenters and incorporates quantum-secure connectivity. Our demonstration provides a blueprint for forward-looking 5G deployments aiming to meet challenging latency and throughput requirements while complying with stringent security requirements. Measurements are performed for network throughput and latency as well as for CPU load of the different components of the 5G deployment, including distributed unit, centralised unit control and user planes, 5G core and the RAN intelligent controller. Additionally, an intelligent workload prediction mechanism, based on an LSTM model, enables preemptive scaling of the centralised unit’s user plane. This proactive approach helps to mitigate bottlenecks as the number of users connecting to the network increases.

Public blockchains and distributed ledger technologies (DLTs) provide reliable distributed, decentralized datasharing, with consensus mechanisms and cryptographic security, data integrity, transparency and trust. Though this transparency and open access allows malicious actors to record arbitrary information to the DLT by exploiting free-form editable text fields in request headers or transaction bodies. The authors propose an architecture that runs external agents interfaced with blockchains, capable of filtering unwanted content to mitigate this threat. Implementation of a resource description framework (RDF) based verification mechanism for blockchain input fields, and an alternate approach using natural language processing (NLP) are then described. Finally, the efficiency and effectiveness of the solution is demonstrated on a blockchain deployment.

Event streaming systems (e.g., Apache Kafka, Apache Pulsar) are a popular substrate for ingesting data with low latency from continuous data sources, such as sensors, cameras, or server logs. Due to the sheer amount of data being stored as data streams, several systems are incorporating storage tiering as a core feature. However, in some cases, the design of the streaming system assumes a reliable connection with the external storage to offload data. This may not be the case when deploying streaming pipelines in the Cloud-Edge Continuum. In this paper, we evaluate deploying a streaming storage system with integrated data tiering (Pravega) in the CloudEdge Continuum. We identify that while Pravega provides good 10 performance, extended unavailability of the long-term storage service may impact stream data ingestion. This can be problematic in Edge use cases with stringent streaming ingestion and processing requirements. To mitigate this problem, we explore the concept of multi-tier long-term storage in Pravega. We implement this concept by integrating an ephemeral tiered storage system (GEDS) to augment Pravega with advanced data tiering mechanisms. Our preliminary results show that GEDS can exploit multiple storage tiers that increase by$3.8 x$the tolerance of the streaming system to long-term storage unavailability.

The growing heterogeneity and decentralization in the modern computing paradigm of edge-cloud continuum introduces new constraints on storage systems, such as storage type, associated processors, privacy, scarce resources, compliance, GDPR and geographical restrictions. While existing distributed data and object stores can ensure data availability and fault-tolerance, they are not flexible or dynamic enough to address these diverse set of constraints. In this paper, we introduce a modular policy-driven data placement framework, CATER, designed to seamlessly integrate with existing storage systems and overcome the aforementioned limitations. CATER formulates the data placement problem as an optimization model, incorporating data collocation and hardware constraints. We integrated a pro-totype of CATER with Apache Ozone and conducted experiments and simulations. Results show a 23% improvement in data placement while respecting 100 % of the constraints.

The exponential proliferation of big data and computation-intensive tasks, such as Artificial Intelligence (AI) applications in factories, poses a significant challenge for the current datacenter-focused technological architecture. The “Big data pRocessing and Artificial Intelligence at the Network Edge” (BRAINE) project addresses this problem by introducing an innovative system architecture designed explicitly for compute-intensive edge deployments. BRAINE focuses on decentralizing the computation tasks, enabling a significant reduction in latency, and optimizing the placement of applications within a cloud-edge continuum to ensure optimal operational efficiency. This paper presents the design, implementation, and testing of our novel system architecture in the context of an AI digital twin for factory robotics. Our empirical results indicate substantial improvements in performance metrics such as processing speed and latency compared to traditional architectures and approaches.

Blockchains and related applications continue to grow as an area of technology that can provide advantages over traditional approaches in certain use cases. One such area are data markets. Data market use cases include secure asset transactions, data lineage and data governance between multiple parties and involves data security, immutability, transparency. Data markets require strong privacy guarantees for digital asset procurement. The inherent properties of a blockchain can provide significant benefits in the data-market use case. In fact, there are many commercial platforms currently leveraging blockchain technologies for blockchain-based data markets. Leveraging blockchain for data market use cases creates the opportunity for new logical architectures to preserve privacy in the blockchain ecosystem. This publication proposes a multi-faceted solution for stronger privacy guarantees for digital asset procurement on blockchain-based data markets. An architecture will be detailed which provides better guarantees of privacy to those acquiring digital asserts from blockchain-based data markets by leveraging time-delays, batch requests, anonymization, and virtual “one-time” users to form a layer of obfuscation around requests.