Using NLP and Machine Learning to Detect Data Privacy Violations
Open accessAbstract
Privacy concerns are constantly increasing in different sectors. Regulations such as the EU's General Data Protection Regulation (GDPR) are pressuring organizations to handle the individual's data with reinforced caution. As information systems deal with increasingly large amounts of personal data in essential services, there is a lack of mechanisms to help organizations in protecting the involved data subjects. In this paper, we propose and evaluate the use of Named Entity Recognition as a way to identify, monitor and validate Personally Identifiable Information. In our experiments, we used three of the most well-known Natural Language Processing tools (NLTK, Stanford CoreNLP, and spaCy). First, we assess the effectiveness of the tools with a generic dataset. Then, machine learning models are trained and evaluated with datasets built on data that contain personally identifiable information. The results show that models' performance was highly positive in accurately classifying both generic and more context-specific data. We observe the relationship between the datasets' training size and respective performance and estimate the appropriate size for model training within this context. Furthermore, we discuss how our proposal can effectively act as a Privacy Enhancing Technology as well as the potential risks and associated impacts.
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Silva et al. (2020) evaluated NER to identify and monitor PII, with Stanford CoreNLP and SpaCy achieving classification accuracy rates of 0.84 and 0.86.
“Silva et al. (2020) [56] evaluated the use of NER to identify and monitor Personally Identifiable Information (PII), with Stanford CoreNLP and SpaCy achieving high classification accuracy rates of 0.84 and 0.86, respectively.”
From A systematic review on insider threat detection using natural language processing (2025) · cites this paper as [56]
The Experimental Approach and Data section says the authors evaluated NER tools for detecting PII, including Stanford CoreNLP and spaCy. The Generic Data results report 0.84 and 0.86, respectively, but identify these as F1 scores on the full generic dataset—not classification accuracy rates or results specifically from the PII-containing contracts. (LLM, conf=0.90)
Experimental — accuracy not yet measured
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- Authors
- Silva, P., Goncalves, C., Godinho, C., Antunes, N., & Curado, M.
- Affiliations
- University of Coimbra (PT)
- Venue
- IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 972-977
- OpenAlex
- W3048742277
- Cited by
- 32
- References
- 13
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 6 · 2022: 7 · 2021: 2
- FWCI
- 1.43
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, public-domain)
- Estudo Geral (Universidad de Coimbra) (submittedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Funders
- Horizon 2020 Framework Programme (786713)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- personally identifiable information, named entity recognition, privacy-enhancing technologies, General Data Protection Regulation, personal data protection, training data size, privacy risk assessment
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