CASSED: Context-based Approach for Structured Sensitive Data Detection

[1]  Alan Jovic,et al.  A Survey of Word Embedding Algorithms for Textual Data Information Extraction , 2021, 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO).

[2]  Jeff Heflin,et al.  SeLaB: Semantic Labeling with BERT , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

[3]  Ming Y. Lu,et al.  Synthetic data in machine learning for medicine and healthcare , 2021, Nature Biomedical Engineering.

[4]  Paul Quinn,et al.  The Difficulty of Defining Sensitive Data—The Concept of Sensitive Data in the EU Data Protection Framework , 2020, German Law Journal.

[5]  Tao Qi,et al.  Named Entity Recognition with Context-Aware Dictionary Knowledge , 2020, CCL.

[6]  Isil Dillig,et al.  Sketch-Driven Regular Expression Generation from Natural Language and Examples , 2019, Transactions of the Association for Computational Linguistics.

[7]  Tim Kraska,et al.  Sherlock: A Deep Learning Approach to Semantic Data Type Detection , 2019, KDD.

[8]  Tim Kraska,et al.  VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository , 2019, CHI.

[9]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[10]  C. Dwork,et al.  Exposed! A Survey of Attacks on Private Data , 2017, Annual Review of Statistics and Its Application.

[11]  Doug Downey,et al.  TabEL: Entity Linking in Web Tables , 2015, SEMWEB.

[12]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[13]  Mónica Marrero,et al.  Named Entity Recognition: Fallacies, challenges and opportunities , 2013, Comput. Stand. Interfaces.

[14]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  Vasilis Efthymiou,et al.  Results of SemTab 2021 , 2021, SemTab@ISWC.