Towards Semantic Interpretation of Structured Data Sources in Privacy-Preserving Environments

As the use of sensitive data becomes increasingly prevalent, it is essential to ensure that privacy preserving technologies are effectively utilized. Although relational databases are commonly used for data storage, they may not provide sufficient insights for identifying privacy vulnerabilities. Moreover, the complexity introduced by multiple actors, legal and technical terms poses a challenge in determining the appropriate privacy-preserving configuration for a specific dataset. This paper presents ongoing work towards adding a semantic layer on top of structured data sources for efficient and intelligent use of data in privacy-preserving scenarios. More specifically, we present key research directions for the development of SemCrypt, a novel framework for schema-enrichment through semantic annotations and mappings to Knowledge Bases and domain ontologies so as to: a) interlink and contextually enrich schemata and data in an interoperable manner; b) use the underlying semantics to assist stakeholders in assessing privacy preserving technologies depending on the sensitivity of data in different use cases, such as in health, finance and cyber threat intelligence.