Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks

  • Jan NeerbekEmail author
  • Ira Assent
  • Peter Dolog
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)


State-of-the-art sensitive information detection in unstructured data relies on the frequency of co-occurrence of keywords with sensitive seed words. In practice, however, this may fail to detect more complex patterns of sensitive information. In this work, we propose learning phrase structures that separate sensitive from non-sensitive documents in recursive neural networks. Our evaluation on real data with human labeled sensitive content shows that our new approach outperforms existing keyword based strategies.


Sensitive information Recursive neural networks Data leak prevention Natural text understanding 



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 645198 (Organicity Project) and No. 732240 (Synchronicity Project).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAarhus UniversityAarhusDenmark
  2. 2.Alexandra InstituteAarhusDenmark
  3. 3.Department of Computer ScienceAalborg UniversityAalborgDenmark

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