A Survey on Differential Privacy for Unstructured Data Content

Huge amounts of unstructured data including image, video, audio, and text are ubiquitously generated and shared, and it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technol...

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Bibliographic Details
Published inACM computing surveys Vol. 54; no. 10s; pp. 1 - 28
Main Authors Zhao, Ying, Chen, Jinjun
Format Journal Article
LanguageEnglish
Published 31.01.2022
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Summary:Huge amounts of unstructured data including image, video, audio, and text are ubiquitously generated and shared, and it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before it is shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also discuss their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research.
ISSN:0360-0300
1557-7341
DOI:10.1145/3490237