Cited By
View all- Diera ALell NGarifullina AScherp A(2023)Memorization of Named Entities in Fine-Tuned BERT ModelsMachine Learning and Knowledge Extraction10.1007/978-3-031-40837-3_16(258-279)Online publication date: 22-Aug-2023
A novel approach on the joint de-identification of textual and relational data with a modified mondrian algorithm
Article No.: 14, Pages 1 - 10
Abstract
Traditional approaches for data anonymization consider relational data and textual data independently. We propose rx-anon, an anonymization approach for heterogeneous semi-structured documents composed of relational and textual attributes. We map sensitive terms extracted from the text to the structured data. This allows us to use concepts like k-anonymity to generate a joint, privacy-preserved version of the heterogeneous data input. We introduce the concept of redundant sensitive information to consistently anonymize the heterogeneous data. To control the influence of anonymization over unstructured textual data versus structured data attributes, we introduce a modified, parameterized Mondrian algorithm. We evaluate our approach with two real-world datasets using a Normalized Certainty Penalty score, adapted to the problem of jointly anonymizing relational and textual data. The results show that our approach is capable of reducing information loss by using the tuning parameter to control the Mondrian partitioning while guaranteeing k-anonymity. As rx-anon is a framework approach, it can be reused and extended by other anonymization algorithms, privacy models, and textual similarity metrics.
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Index Terms
- A novel approach on the joint de-identification of textual and relational data with a modified mondrian algorithm
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August 2021
178 pages
ISBN:9781450385961
DOI:10.1145/3469096
- General Chairs:
- Patrick Healy,
- Mihai Bilauca,
- Program Chair:
- Alexandra Bonnici
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Published: 16 August 2021
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View all- Diera ALell NGarifullina AScherp A(2023)Memorization of Named Entities in Fine-Tuned BERT ModelsMachine Learning and Knowledge Extraction10.1007/978-3-031-40837-3_16(258-279)Online publication date: 22-Aug-2023
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