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BioMEMS: Forging New Collaborations Between Biologists and Engineers
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DIFFERENTIALLY PRIVATE OUTLIER DETECTION IN A COLLABORATIVE ENVIRONMENT.

Hafiz Asif1, Tanay Talukdar1, Jaideep Vaidya1

  • 1MSIS Department, Rutgers University, 1 Washington Park Newark, NJ, 07102, USA.

International Journal of Cooperative Information Systems
|January 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a privacy-preserving method for collaborative outlier detection across distributed datasets. It ensures accurate anomaly detection without compromising sensitive information using differential privacy and secure computation.

Keywords:
Distributed DataOutlier DetectionPrivacy

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Area of Science:

  • Data Science
  • Computer Science
  • Cybersecurity

Background:

  • Outlier detection identifies abnormal data points, crucial for many applications.
  • Distributed data across organizations hinders accurate collaborative outlier detection due to privacy concerns.
  • Integrating data centrally is often infeasible due to legal and privacy restrictions.

Purpose of the Study:

  • To develop a privacy-preserving method for collaborative outlier detection on distributed categorical data.
  • To address the challenge of performing accurate outlier analysis without centralizing sensitive datasets.
  • To define privacy within the context of collaborative outlier detection.

Main Methods:

  • A novel method for outlier detection in horizontally and vertically partitioned categorical data.
  • Leveraging a scalable outlier detection technique based on attribute value frequencies.
  • Employing differential privacy and secure multiparty computation for end-to-end privacy guarantees.

Main Results:

  • The proposed technique effectively identifies outliers in distributed datasets while preserving privacy.
  • Experimental results demonstrate the method's efficiency and accuracy on real-world data.
  • Achieved accurate collaborative outlier detection without data integration.

Conclusions:

  • The developed method enables effective and efficient privacy-preserving collaborative outlier detection.
  • It overcomes privacy and legal barriers to distributed data analysis.
  • Offers a robust solution for anomaly detection in sensitive, decentralized data environments.