Task-Specific Adaptive Differential Privacy Method for Structured Data

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.

SSKM_DP: Differential Privacy Data Publishing Method via SFLA-Kohonen Network

“…Zhang [4] proposed a data publishing privacy protection method based on local priority anonymity (LPA), which automatically selects anonymous technology for each anonymous algorithm. Utaliyeva [5] believes that anonymity technology is vulnerable to various attacks and proposed an adaptive differential privacy protection method for structured data. It protects the privacy of sensitive information through machine learning (ML), which solves the privacy-utility trade-off problem.…”