The Function-as-a-Service cloud computing paradigm has made large-scale application development convenient and efficient as developers no longer need to deploy or manage the necessary infrastructure themselves. However, as a consequence of this abstraction, developers lose insight into how their code is executed and data is processed. Cloud providers currently offer little to no assurance of the integrity of customer data. One approach to robust data integrity verification is the analysis of data provenance—logs that describe the causal history of data, applications, users, and non-person entities. This paper introduces ProProv, a new domain-specific language and graphical user interface for specifying policies over provenance metadata to automate provenance analyses.To evaluate the convenience and usability of the new ProProv interface, 61 individuals were recruited to construct provenance policies using both ProProv and the popular, general-purpose policy specification language Rego—used as a baseline for comparison. We found that, compared to Rego, the ProProv interface greatly increased the number of policies successfully constructed, improved the time taken to construct those policies, and reduced the failed-attempt rate. Participants successfully constructed 73% of the requested policies using ProProv, compared to 41% using Rego. To further evaluate the usability of the tools, participants were given a 10-question questionnaire measured using the System Usability Scale (SUS). The median SUS score for the graphical ProProv interface was above average and fell into the “excellent” category, compared to below average and “OK” for Rego. These results highlight the impacts that graphical domain-specific tools can have on the accuracy and speed of policy construction.
Paper
Full text
ProProv: A Language and Graphical Tool for Specifying Data Provenance Policies
Semantic Scholar · Computer Science · 2022
Abstract
The Function-as-a-Service cloud computing paradigm has made large-scale application development convenient and efficient as developers no longer need to deploy or manage the necessary infrastructure themselves. However, as a consequence of this abstraction, developers lose insight into how their code is executed and data is processed. Cloud providers currently offer little to no assurance of the integrity of customer data. One approach to robust data integrity verification is the analysis of data provenance—logs that describe the causal history of data, applications, users, and non-person entities. This paper introduces ProProv, a new domain-specific language and graphical user interface for specifying policies over provenance metadata to automate provenance analyses.To evaluate the convenience and usability of the new ProProv interface, 61 individuals were recruited to construct provenance policies using both ProProv and the popular, general-purpose policy specification language Rego—used as a baseline for comparison. We found that, compared to Rego, the ProProv interface greatly increased the number of policies successfully constructed, improved the time taken to construct those policies, and reduced the failed-attempt rate. Participants successfully constructed 73% of the requested policies using ProProv, compared to 41% using Rego. To further evaluate the usability of the tools, participants were given a 10-question questionnaire measured using the System Usability Scale (SUS). The median SUS score for the graphical ProProv interface was above average and fell into the “excellent” category, compared to below average and “OK” for Rego. These results highlight the impacts that graphical domain-specific tools can have on the accuracy and speed of policy construction.