A Zero Trust Model Based Framework For Data Quality Assessment
| Title | A Zero Trust Model Based Framework For Data Quality Assessment |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Mohammed, Mahmood, Talburt, John R., Dagtas, Serhan, Hollingsworth, Melissa |
| Conference Name | 2021 International Conference on Computational Science and Computational Intelligence (CSCI) |
| Keywords | Acoustic Fingerprints, composability, Computational modeling, Costs, data integrity, Data models, data quality, data quality assessment, Data quality dimensions, Human Behavior, human factors, Industries, Organizations, pubcrawl, resilience, Resiliency, Scientific computing, Trusted Data, zero trust |
| Abstract | Zero trust security model has been picking up adoption in various organizations due to its various advantages. Data quality is still one of the fundamental challenges in data curation in many organizations where data consumers don't trust data due to associated quality issues. As a result, there is a lack of confidence in making business decisions based on data. We design a model based on the zero trust security model to demonstrate how the trust of data consumers can be established. We present a sample application to distinguish the traditional approach from the zero trust based data quality framework. |
| DOI | 10.1109/CSCI54926.2021.00123 |
| Citation Key | mohammed_zero_2021 |