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Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent.

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Area of Science:

  • Machine Learning
  • Privacy-Preserving Techniques
  • Optimization Algorithms

Background:

  • Protecting sensitive data in machine learning is critical.
  • Private Gradient Descent (PGD) uses noise for privacy, but its effectiveness with dynamic schedules is unclear.
  • Dynamic privacy schedules may improve final model loss but lack theoretical understanding.

Purpose of the Study:

  • To provide a comprehensive analysis of noise influence in dynamic privacy schedules for PGD.
  • To understand the theoretical effectiveness of dynamic noise reduction in private learning.
  • To investigate the connection between dynamic noise schedules and optimization algorithms.

Main Methods:

  • Developed a dynamic noise schedule to minimize the utility upper bound of PGD.
  • Analyzed the collective impact of noise from each optimization step on model utility.
  • Investigated the influence of dynamic noise when using momentum in optimization.
  • Empirically validated findings on general non-convex loss functions.

Main Results:

  • Presented a novel dynamic noise schedule for PGD.
  • Demonstrated how noise influence from individual steps collectively impacts final model utility.
  • Showed that dynamic noise influence changes when momentum is incorporated.
  • Confirmed the connection between dynamic noise and optimization for non-convex losses.

Conclusions:

  • Dynamic privacy schedules offer a promising direction for improving PGD utility.
  • Understanding noise influence is key to designing effective dynamic privacy strategies.
  • Loss curvature significantly impacts the effectiveness of dynamic noise in private learning.