41 citations · 54 across the 15 of their papers we have counts for
9 papers · 1 filter
AdProv: A Method for Provenance of Process Adaptations
Ludwig Stage, Mirela Riveni, Raimundas Matulevičius +1
Provenance in scientific workflows is essential for understand- ing and reproducing processes, while in business processes, it can ensure compliance and correctness and facilitates…
Reinforcement Learning-Driven Adaptation Chains: A Robust Framework for Multi-Cloud Workflow Security
Nafiseh Soveizi, Dimka Karastoyanova
Cloud computing has emerged as a crucial solution for managing data- and compute-intensive workflows, offering scalability to address dynamic demands. However, security concerns pe…
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…
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…
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…
Verifiable Privacy-Preserving Computing
Tariq Bontekoe, Dimka Karastoyanova, Fatih Turkmen
Privacy-preserving computation (PPC) methods, such as secure multiparty computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confide…
Security and Privacy Concerns in Cloud-based Scientific and Business Workflows: A Systematic Review
Nafiseh Soveizi, Fatih Turkmen, Dimka Karastoyanova
Today, the number of data-intensive and compute-intensive applications like business and scientific workflows has dramatically increased, which made cloud computing more popular in…
To remove or not remove Mobile Apps? A data-driven predictive model approach
Fadi Mohsen, Dimka Karastoyanova, George Azzopardi
Mobile app stores are the key distributors of mobile applications. They regularly apply vetting processes to the deployed apps. Yet, some of these vetting processes might be inadeq…
Blockchain for Genomics: A Systematic Literature Review
Mohammed Alghazwi, Fatih Turkmen, Joeri van der Velde +1
Human genomic data carry unique information about an individual and offer unprecedented opportunities for healthcare. The clinical interpretations derived from large genomic datase…