Privacy Preserving Data Quality Assessment for High-Fidelity Data Sharing

In a data-driven economy that struggles to cope with the volume and diversity of information, data quality assessment has become a necessary precursor to data analytics. Real-world data often contains inconsistencies, conflicts and errors. Such dirty data increases processing costs and has a negative impact on analytics. Assessing the quality of a dataset is especially important when a party is considering acquisition of data held by an untrusted entity. In this scenario, it is necessary to consider privacy risks of the stakeholders.