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Human intuition as a defense against attribute inference.

Marcin Waniek1, Navya Suri1, Abdullah Zameek1

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People struggle to protect their privacy from attribute inference compared to AI. AI is more effective at hiding personal data, highlighting the need for algorithmic privacy solutions.

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Area of Science:

  • Computer Science
  • Social Sciences
  • Information Science

Background:

  • Attribute inference, using public data to uncover hidden information, poses a significant privacy threat due to advancements in machine learning.
  • Modifying publicly available data is a strategy to mitigate attribute inference risks and protect private information.

Purpose of the Study:

  • To evaluate human effectiveness in protecting data privacy against attribute inference.
  • To compare human performance with AI algorithms in data modification for privacy preservation.
  • To identify critical data aspects for inference algorithms and human limitations in recognizing them.

Main Methods:

  • Comparative analysis of human versus AI performance in attribute inference tasks.
  • Focus on three distinct attributes: author gender in text, photo geolocation, and social network connections.
  • Assessment of data modification strategies employed by both humans and AI.

Main Results:

  • Human performance in preventing attribute inference is inferior to AI.
  • AI demonstrates superior effectiveness in hiding sensitive attributes within data.
  • Humans are less likely than AI to implement high-impact modifications for privacy protection.

Conclusions:

  • Human intuition is insufficient for safeguarding privacy against sophisticated AI-driven attribute inference.
  • Algorithmic approaches are essential for robust protection of private information in the era of AI.
  • Understanding data criticality for inference is a key differentiator between human and AI privacy strategies.