most citedTrusted Provenance of Automated, Collaborative and Adaptive Data Processing Pipelines

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2024

VPAS: Publicly Verifiable and Privacy-Preserving Aggregate Statistics on Distributed Datasets

Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova +1

Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healt…

cs.CR20231 cited

Trusted Provenance of Automated, Collaborative and Adaptive Data Processing Pipelines

Ludwig Stage, Dimka Karastoyanova

To benefit from the abundance of data and the insights it brings data processing pipelines are being used in many areas of research and development in both industry and academia. O…

cs.CR2023

Enhancing Workflow Security in Multi-Cloud Environments through Monitoring and Adaptation upon Cloud Service and Network Security Violations

Nafiseh Soveizi, Dimka Karastoyanova

Cloud computing has emerged as a crucial solution for handling data- and compute-intensive workflows, offering scalability to address dynamic demands. However, ensuring the secure…

cs.CV2023

Deep supervised hashing for fast retrieval of radio image cubes

Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds +2

The shear number of sources that will be detected by next-generation radio surveys will be astronomical, which will result in serendipitous discoveries. Data-dependent deep hashing…

astro-ph.IM2023

Advances on the classification of radio image cubes

Steven Ndung'u, Trienko Grobler, Stefan J. Wijnholds +2

Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare…