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Anonymization: The imperfect science of using data while preserving privacy.

Andrea Gadotti^1,2, Luc Rocher^1,2, Florimond Houssiau^1,3

¹Imperial College London, Exhibition Road, London SW7 2AZ, UK.

Science Advances

|July 17, 2024

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Safely sharing data requires modern anonymization techniques, not just traditional de-identification. Auditing these methods ensures privacy protection for scientific advancement.

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Area of Science:

Data privacy and security
Information science
Computer science

Background:

Vast amounts of personal data are generated from surveys, studies, and digital devices.
Safe data sharing is crucial for scientific and societal progress.
Anonymization is a primary method for minimizing privacy risks in data sharing.

Purpose of the Study:

To provide a pragmatic review of modern privacy attacks and anonymization techniques.
To discuss the limitations of traditional de-identification methods in the big data era.
To explore contemporary approaches for sharing anonymous aggregate data.

Main Methods:

Review of current literature on privacy attacks.
Analysis of traditional de-identification techniques and their shortcomings.
Examination of modern anonymization methods including data query systems, synthetic data, and differential privacy.

Main Results:

Traditional de-identification methods are insufficient for big data.
Modern techniques like differential privacy offer improved data anonymization.
No single anonymization solution is perfect.

Conclusions:

Modern anonymization techniques are essential for safe data use and sharing.
Auditing the guarantees of these techniques against privacy attacks is critical.
A combination of modern methods and rigorous auditing provides the best current approach to data privacy.