TY  - JOUR
T1  - Two-Fold Differentially Private Mechanism for Big Data Analysis
AU  - Utaliyeva, Assem 
AU  - Choi, Yoon-Ho 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2024
DA  - 2024/1/1
DO  - 10.7840/kics.2024.49.3.393
KW  - Differential Privacy
KW  - ML for Big Data
KW  - Privacy Preservation
AB  - Differential privacy (DP) has emerged as a gold standard for privacy preservation in many applications, particularly in light of recent advancements in machine learning for big data for cloud services and the growing threat of privacy attacks. However, the addition of random noise to data for privacy preservation often results in decreased data quality and utility. To address this challenge, we propose a novel twofold differentially private data generation method that leverages the power of denoising autoencoders to preserve higher data quality and utility. Our approach combines traditional additive differential privacy with a novel reductive differential privacy approach that uses a denoising autoencoder to restore the original distribution of the data, increasing the data utility in machine learning tasks. We also experimentally show the effectiveness of the proposed method by experimental evaluation.