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Related Experiment Video

Updated: Jul 11, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Identifying Anomalies while Preserving Privacy.

Hafiz Asif¹, Jaideep Vaidya¹, Periklis A Papakonstantinou¹

¹Rutgers University, New Jersey, USA.

IEEE Transactions on Knowledge and Data Engineering

|November 17, 2023

Summary

This summary is machine-generated.

Sensitive privacy enables accurate anomaly detection while protecting data. This work introduces a novel mechanism for efficient, private outlier analysis, ensuring strong privacy guarantees.

Keywords:

anomaly identification differential privacy outlier analysis outlier detection sensitive privacy

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Area of Science:

Computer Science
Data Privacy
Machine Learning

Background:

Data anomaly detection is crucial across various sectors like medicine and finance.
Existing privacy methods hinder accurate outlier analysis.
Sensitive privacy offers a robust solution, balancing accuracy and strong privacy guarantees.

Purpose of the Study:

To connect sensitive privacy with other data privacy concepts.
To develop efficient mechanisms for sensitive privacy-preserving anomaly detection.
To analyze the optimality of these mechanisms.

Main Methods:

Relating sensitive privacy to established data privacy notions.
Developing a novel n-step lookahead mechanism for outlier queries.
Providing general constructions for sensitively private anomaly identification.

Main Results:

Sensitive privacy allows anomaly analysis with high accuracy and strong privacy.
The proposed n-step lookahead mechanism efficiently answers arbitrary outlier queries.
General constructions for private anomaly detection mechanisms are presented.

Conclusions:

Sensitive privacy is a viable approach for accurate, private anomaly detection.
The developed mechanisms provide provable sensitive privacy guarantees for common anomaly models.
The study offers insights into optimal private mechanism constructions for anomaly identification.