Synthetic data generation has emerged as a powerful solution to meet the demand for high-quality, diverse, and privacy-preserving data in many domains. Still, there is an open challenge when dealing with class imbalance and privacy preservation in synthetic tabular data generation. Thus, this study introduces two algorithms: balanced Tabular Generative Adversarial Network (b-TGAN) and balanced Tabular Principle Component Analysis (b-TPCA). While b-TGAN proactively tackles class imbalance by incorporating a re-balancing mechanism and leveraging an Autoencoder, b-TPCA offers a privacy-preserving solution by generating synthetic data using statistical properties. Through experiments on five datasets, this study demonstrates the effectiveness of b-TGAN in generating balanced data, particularly in improving the performance on minority classes. b-TPCA also shows promising results, achieving comparable ML utility to the baseline method while enhancing privacy preservation.