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Related Experiment Videos

Utility-preserving transaction data anonymization with low information loss.

Grigorios Loukides¹, Aris Gkoulalas-Divanis

¹School of Computer Science & Informatics, Cardiff University, UK.

Expert Systems with Applications

|May 8, 2012

Summary

This study introduces a novel data anonymization method that preserves data utility while minimizing information loss. Our approach significantly enhances query answering accuracy for sensitive transaction data in marketing and medicine.

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Area of Science:

Computer Science
Information Security
Data Science

Background:

Transaction data, including purchases and diagnoses, are vital for large-scale studies in marketing and medicine.
Disseminating this data risks privacy breaches by enabling individual identity linkage.
Existing anonymization methods often sacrifice data utility for privacy, leading to excessive distortion.

Purpose of the Study:

To propose a novel approach for anonymizing transaction data.
To satisfy data publishers' utility requirements while minimizing information loss.
To develop an accurate information loss measure and an effective anonymization algorithm.

Main Methods:

Introduced a novel information loss measure.
Developed an effective anonymization algorithm exploring a large problem space.
Conducted extensive experiments using click-stream and medical data.

Main Results:

The proposed approach achieves significantly higher query answering accuracy compared to state-of-the-art methods.
The method demonstrates comparable efficiency to existing techniques.
Experimental results validate the approach's ability to maintain data utility.

Conclusions:

The novel anonymization approach effectively balances privacy preservation with data utility.
This method offers a superior solution for secure and useful transaction data analysis.
The findings have significant implications for privacy-preserving data sharing in research and industry.