人类的直觉作为对属性推断的防御.
Marcin Waniek1, Navya Suri1, Abdullah Zameek1
1Computer Science, Science Division, New York University Abu Dhabi, Abu Dhabi, UAE.
Scientific reports
|September 26, 2023
概括
与AI相比,人们努力保护他们的隐私免受属性推断的影响. 人工智能更有效地隐藏个人数据,突出了对算法隐私解决方案的需求.
科学领域:
- 计算机科学 计算机科学
- 社会科学 社会科学 社会科学
- 信息科学 信息科学 信息科学
背景情况:
- 使用公共数据来发现隐藏信息的属性推断,由于机器学习的进步,造成了重大隐私威胁.
- 修改公开可用的数据是一种减轻属性推断风险和保护私人信息的策略.
研究的目的:
- 评估人类在保护数据隐私方面的有效性,以防止属性推断.
- 将人类性能与人工智能算法进行数据修改以保护隐私的比较.
- 识别推断算法的关键数据方面以及人类识别它们的局限性.
主要方法:
- 在属性推断任务中对人类与人工智能性能进行比较分析.
- 专注于三个不同的属性:作者在文本中的性别,照片地理位置和社交网络连接.
- 评估人类和人工智能使用的数据修改策略.
主要成果:
- 人类在防止属性推断方面的表现低于人工智能.
- 人工智能在隐藏数据中的敏感属性方面表现出卓越的有效性.
- 人类不太可能比人工智能实施高影响的修改来保护隐私.
结论:
- 人类的直觉不足以保护隐私免受复杂的AI驱动的属性推断.
- 在AI时代,算法方法对于强有力的私人信息保护至关重要.
- 了解推理数据的关键性是人类和人工智能隐私策略之间的关键差异化因素.
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