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Task-Specific Adaptive Differential Privacy Method for Structured Data. | LitMetric

Task-Specific Adaptive Differential Privacy Method for Structured Data.

Sensors (Basel)

School of Computer Science and Engineering, Pusan National University, Busan 609-735, Republic of Korea.

Published: February 2023

AI Article Synopsis

  • Machine learning requires data for training, often involving private datasets with sensitive information, leading to a need for effective anonymization techniques to preserve privacy.* -
  • Current anonymization methods are not completely secure and can be vulnerable to privacy attacks that reveal sensitive data, necessitating improvements in privacy-preserving strategies.* -
  • The proposed task-specific adaptive differential privacy technique customizes the noise added to data based on feature importance for specific ML tasks, demonstrating effectiveness across various datasets and maintaining a balance between privacy and utility.*

Article Abstract

Data are needed to train machine learning (ML) algorithms, and in many cases often include private datasets that contain sensitive information. To preserve the privacy of data used while training ML algorithms, computer scientists have widely deployed anonymization techniques. These anonymization techniques have been widely used but are not foolproof. Many studies showed that ML models using anonymization techniques are vulnerable to various privacy attacks willing to expose sensitive information. As a privacy-preserving machine learning (PPML) technique that protects private data with sensitive information in ML, we propose a new task-specific adaptive differential privacy (DP) technique for structured data. The main idea of the proposed DP method is to adaptively calibrate the amount and distribution of random noise applied to each attribute according to the feature importance for the specific tasks of ML models and different types of data. From experimental results under various datasets, tasks of ML models, different DP mechanisms, and so on, we evaluate the effectiveness of the proposed task-specific adaptive DP method. Thus, we show that the proposed task-specific adaptive DP technique satisfies the model-agnostic property to be applied to a wide range of ML tasks and various types of data while resolving the privacy-utility trade-off problem.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9966464PMC
http://dx.doi.org/10.3390/s23041980DOI Listing

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