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A New Wavelet-Based Privatization Mechanism for Probability Distributions.

Hélio M de Oliveira1, Raydonal Ospina1, Víctor Leiva2

  • 1Department of Statistics, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil.

Sensors (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wavelet-based privatization mechanism for data, ensuring privacy by perturbing probability distributions. This method enhances data fitting techniques for AI and machine learning applications.

Keywords:
artificial intelligencedata fittingdatabase-sensordigital image sensormachine learningperturbation theorysignal-to-noise ratiostatistical modelingwavelets

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Area of Science:

  • Data Science
  • Signal Processing
  • Applied Mathematics

Background:

  • Wavelets are effective for extracting information from unstructured data like sensor signals.
  • Existing data fitting techniques in AI/ML require enhancement for privacy preservation.

Purpose of the Study:

  • To propose a new wavelet-based mechanism for data privatization.
  • To develop a mathematical method for selecting appropriate probability distributions for data.

Main Methods:

  • Utilizing wavelets, specifically the cumulative wavelet integral function, to perturb probability distributions.
  • Demonstrating that additively perturbed distribution functions remain valid distribution functions.

Main Results:

  • A novel privatization mechanism based on wavelet perturbation is established.
  • The perturbed distribution functions are shown to be valid, representing privatized distributions.
  • Computational experiments validated the method using sensor data and algorithms.

Conclusions:

  • The proposed wavelet function serves as an effective privatization mechanism.
  • This approach offers a mathematical framework for choosing probability distributions.
  • The method has significant implications for artificial intelligence, machine learning, and deep learning.