Towards a Free Text Dataset for Hiding Quasi-Identifiers
Article No.: 89, Pages 1 - 6
Abstract
Personal information protection is becoming so important for individuals. Besides personal identifier information (PII), quasi-identifier information (QII) also needs protection, as the community argues, and the solution methods have attracted many research. Many existing methods for protecting QII focus on structured text data which is organized by tables of records. However, free text data that contains QII, is very common in application domains, such as data lakes of a company. The protection of QII in free text data thus need new methods. Supervised machine learning based solutions are promising while usually require a large scale dataset to train the model. Here we propose a novel method towards building such a desired dataset. Our method exploits an existing structured text dataset, a table to sentence generation deep learning model, and incorporated the idea of Piecewise Convolution Neural Network (PCNN). The resulted dataset contains more than 120,000 free text sentences, and many of them contains QII data.
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2022
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Qualifiers
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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