aggiornato main

main.tex

@@ -61,6 +61,8 @@
   \begin{abstract}
     The conflict between the need of protecting and sharing data is hampering the spread of big data applications. Proper security and privacy assurance is required to protect data owners, while proper data access and sharing are fundamental to implement smart big data solutions. In this context, access control systems assume a central role for balancing the need of data protection and sharing. However, given the software and technological complexity of big data ecosystems, existing solutions are not suitable because they are neither general nor scalable, and do not support a dynamic and collaborative environment. In this paper, we propose an access control system that enforces access to data in a distributed, multi-party big data environment. It is based on data annotations and secure data transformations performed at ingestion time. We show the feasibility of our approach with a case study in a smart city domain using an Apache-based big data engine.
 
+    In today's data landscape, the coexistence of data quality and data privacy is critical to support high-value services and pipelines.
+Our approach seeks to harmonize these objectives by establishing a data governance framework that balances privacy and data quality.
   \end{abstract}
 
   \begin{IEEEkeywords}
@@ -92,7 +94,7 @@
 }
 
 \input{introduction}
-\input{motivations}
+%\input{motivations}
 \input{system_model}
 \input{pipeline_template.tex}
 \input{pipeline_template_example.tex}