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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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A Guide for Private Outlier Analysis.

Hafiz Asif1, Periklis A Papakonstantinou1, Jaideep Vaidya1

  • 1MSIS Department, Rutgers University, USA.

IEEE Letters of the Computer Society
|August 18, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed a general framework for private outlier analysis to protect sensitive data. This two-step method identifies problem specifications and offers a practical solution for privacy-preserving data analytics.

Keywords:
anomalydifferential privacyoutliersprivacysecuritysensitive privacy

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Area of Science:

  • Computer Science
  • Data Science
  • Information Security

Background:

  • Growing societal demand for data privacy necessitates privacy-preserving methods in data analysis.
  • Outlier analysis is crucial for applications in medicine, finance, and national security.
  • Existing privacy solutions for outlier analysis are limited to specialized cases.

Purpose of the Study:

  • To establish the first general framework for private outlier analysis.
  • To address the gap in privacy preservation for fundamental data analytics tasks.
  • To provide a practical and formally verified solution for privacy in outlier detection.

Main Methods:

  • A two-step process is introduced for private outlier analysis.
  • The first step involves identifying relevant problem specifications for privacy.
  • The second step provides a practical solution that formally meets identified specifications.

Main Results:

  • A general framework for private outlier analysis has been established.
  • The framework addresses the need for privacy in a fundamental data analytics task.
  • A practical and formally verified solution is presented.

Conclusions:

  • This work provides a foundational framework for private outlier analysis.
  • The proposed method enables privacy preservation in critical data analytics applications.
  • The research offers a generalizable solution for a previously specialized problem.