A Survey on Differential Privacy for Unstructured Data Content

Ying Zhao, Jinjun Chen
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引用次数: 103

Abstract

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.
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非结构化数据内容的差异隐私研究
包括图像、视频、音频和文本在内的大量非结构化数据无处不在地生成和共享,保护其中的敏感个人信息(如人脸、声纹和作者)是一项挑战。差分隐私是标准的隐私保护技术,为各种数据提供严格的隐私保障。本调查总结并分析了不同的隐私解决方案,以在非结构化数据内容与不受信任的各方共享之前保护它们。这些差分隐私方法在用向量表示非结构化数据后对其进行模糊处理,然后用模糊处理后的向量对其进行重构。我们总结了具体的隐私模型和机制,以及它们可能面临的挑战。我们还讨论了他们对人工智能攻击和效用损失的隐私保障。最后,讨论了未来研究的几个可能方向。
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