Perturbation Methods for Protecting Data Privacy: A Review of Techniques and Applications

Safiye Turgay; İlker İlter

doi:10.23977/autml.2023.040205

Perturbation Methods for Protecting Data Privacy: A Review of Techniques and Applications

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DOI: 10.23977/autml.2023.040205 | Downloads: 46 | Views: 713

Author(s)

Safiye Turgay ¹, İlker İlter ¹

Affiliation(s)

¹ Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

Corresponding Author

Safiye Turgay

ABSTRACT

Perturbation methods are mathematical techniques used to add controlled noise or randomness to data to protect privacy while allowing data analysis. Various methods, such as randomized response, differential privacy, secure multi-party computation, noise addition, and sampling and aggregation, are used to protect sensitive information from disclosure or exploitation. These methods have been successfully applied in machine learning, statistics, and cryptography to ensure data privacy. However, their implementation must be carefully designed to avoid compromising data accuracy or introducing bias in analysis. Mostly, perturbation methods offer a promising approach to protect data privacy in various fields. This review provides an overview of perturbation methods used to protect data privacy in various fields, including machine learning, statistics, and cryptography. Perturbation methods involve adding controlled noise or randomness to data to preserve privacy while still allowing data analysis.

KEYWORDS

Privacy; Data mining protecting privacy; Big data; Multi-dimensional grid; Privacy enhancement; Perturbation approach

CITE THIS PAPER

Safiye Turgay, İlker İlter, Perturbation Methods for Protecting Data Privacy: A Review of Techniques and Applications . Automation and Machine Learning (2023) Vol. 4: 31-41. DOI: http://dx.doi.org/10.23977/autml.2023.040205.

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Perturbation Methods for Protecting Data Privacy: A Review of Techniques and Applications

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ABSTRACT

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