相关论文: HyperProv: Decentralized Resilient Data Provenance…

The emergence of Cloud computing provides a new computing paradigm for scientific workflow execution. It provides dynamic, on-demand and scalable resources that enable the processing of complex workflow-based experiments. With the ever…

The rapid advancement of large language models (LLMs) in recent years has revolutionized the AI landscape. However, the deployment model and usage of LLM services remain highly centralized, creating significant trust issues and costs for…

Permissioned blockchains ensure integrity and auditability of shared data but expose query parameters to peers during read operations, creating privacy risks for organizations querying sensitive records. This paper proposes a Private…

A big challenge posed in blockchain centric platforms is achieving scalability while also preserving user privacy. This report details the design, implementation and evaluation of a Layer-2 scaling solution for Hyperledger Fabric using Zero…

The rise of crypto-currencies has spawned great interest in their underlying technology, namely, Blockchain. The central component in a Blockchain is a shared distributed ledger. A ledger comprises series of blocks, which in turns contains…

Federated Learning (FL) presents a promising paradigm for training machine learning models across decentralized edge devices while preserving data privacy. Ensuring the integrity and traceability of data across these distributed…

Decentralized computation outsourcing should allow anyone to access the large amounts of computational power that exists in the Internet of Things. Unfortunately, when trusted third parties are removed to achieve this decentralization,…

In this paper, we investigate how we can leverage Spark platform for efficiently processing provenance queries on large volumes of workflow provenance data. We focus on processing provenance queries at attribute-value level which is the…

Blockchain technology and related frameworks have recently received extensive attention. Blockchain systems use multi-party consensus protocols to reach agreements on transactions. Hyperledger Fabric framework exposes a multi-party…

Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems - often systems-of-systems - poses accountability challenges. A key reason…

As the demand for large scale AI models continues to grow, the optimization of their training to balance computational efficiency, execution time, accuracy and energy consumption represents a critical multidimensional challenge. Achieving…

"Trustworthy data" is the fuel for ensuring transparent traceability, precise decision-making, and cogent coordination in the supply chain (SC) space. However, the disparate data silos act as a trade barrier in orchestrating the provenance…

Logic programming languages such as Datalog have become popular as Domain Specific Languages (DSLs) for solving large-scale, real-world problems, in particular, static program analysis and network analysis. The logic specifications which…

The rapid advancement of generative AI systems has collapsed the credibility landscape for photographic evidence. Modern image generation models produce photorealistic images undermining the evidentiary foundation upon which journalism and…

Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern. However, traditional consensus…

In this paper, we present the design of a scalable, distributed stream processing system for RFID tracking and monitoring. Since RFID data lacks containment and location information that is key to query processing, we propose to combine…

Proof-of-Work (PoW) blockchain consensus consumes vast computational resources without producing useful output, while the rapid growth of large language model (LLM) agents has created unprecedented demand for GPU computation. We present…

Sharing provenance across workflow management systems automatically is not currently possible, but the value of such a capability is high since it could greatly reduce the amount of duplicated workflows, accelerate the discovery of new…

As data-driven methods are becoming pervasive in a wide variety of disciplines, there is an urgent need to develop scalable and sustainable tools to simplify the process of data science, to make it easier to keep track of the analyses being…

Edge computing is a distributed computing paradigm that relies on computational resources of end devices in a network to bring benefits such as low bandwidth utilization, responsiveness, scalability and privacy preservation. Applications…