PriTxt: A privacy risk assessment method for text data based on semantic correlation learning. (3rd November 2021)

• Record Type: Journal Article
• Authors: Xiong, Ping; Liang, Lin; Zhu, Yunli; Zhu, Tianqing
• Is Part Of: Concurrency and computation. Volume 34:Number 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-03
• Subjects: GDPR -- privacy preservation -- privacy risk -- unstructured text data; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6680
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Privacy risk assessment plays a fundamental role in privacy preservation, as it determines the extent to which subsequent processing (such as generalization and obfuscation), should be applied to the sensitive data. However, most existing works on privacy risk assessment have focused on structured data, while unstructured text data remain relatively underexplored due to the complexity of natural language. In this article, we propose a novel method, PriTxt, for evaluating the privacy risk associated with text data by exploiting the semantic correlation. Using definitions derived from the General Data Protection Regulation (GDPR), a de facto standard of privacy preservation in practice, PriTxt first defines the private features that related to individual privacy in order to locate the sensitive words. By using the word2vec algorithm, a word‐embedding model is further constructed to identify the quasi‐sensitive words that are semantically correlated to the private features. The privacy risk of a given text is finally evaluated by aggregating the weighted risks of the sensitive and the quasi‐sensitive words in the text. Experiments on real‐world datasets demonstrate that the proposed PriTxt is effective for conducting risk assessment on text data and further outperforms the traditional methods.