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Local Privacy Protection for Sensitive Areas in Multiface Images.

Chao Liu1,2, Jing Yang1, Xuan Zhang3

  • 1College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.

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|March 25, 2022
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This summary is machine-generated.

This study introduces a privacy protection algorithm (PPSA) for sensitive face areas in multiple images. The PPSA algorithm enhances accuracy and recall for facial recognition systems while ensuring differential privacy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Face recognition systems face privacy challenges in accurately identifying individuals.
  • Existing privacy protection methods may not effectively handle multiface images.
  • Goal-driven reasoning offers a novel approach to local privacy protection.

Purpose of the Study:

  • To design a goal-driven algorithm for local privacy protection in sensitive areas of multiface images.
  • To integrate face recognition, regional growth, and differential privacy within an interactive framework.
  • To develop a privacy protection for sensitive areas (PPSA) algorithm that addresses multiface scenarios.

Main Methods:

  • Utilized multitask cascaded convolutional network (MTCNN) for face and landmark recognition.
  • Employed regional growth seeded by overlapping subgraphs and guided by fusion similarity measurement mechanism (FSMM).
  • Implemented a novel privacy budget allocation strategy dividing the total budget (ε) into ε_1 and ε_2 for targeted protection.

Main Results:

  • The PPSA algorithm ensures ε-differential privacy for multiface images.
  • Noise error in PPSA is independent of image size, unlike the Laplacian algorithm.
  • Achieved significant improvements: accuracy (≥16.1%), recall (≥2.3%), and F1-score (≥15.2%) in image classification tasks.

Conclusions:

  • The PPSA algorithm offers effective and efficient local privacy protection for sensitive face areas in multiface images.
  • The proposed privacy budget allocation enhances the performance of privacy protection in complex scenarios.
  • PPSA demonstrates superior performance compared to existing methods, meeting differential privacy requirements.