Model quantization has become a fundamental technique for enabling the efficient deployment of large-scale deep neural networks (DNNs) in data-intensive applications by reducing model size. Despite its widespread adoption, the impact of quantization on training data privacy --- particularly vulnerability to membership inference attacks (MIAs) --- remains largely unexplored. In this paper, we present the first comprehensive empirical study of privacy risks in quantized DNNs, evaluating eight state-of-the-art MIA attacks across six representative quantization methods. Our results reveal that practical quantization levels (4-8 bits) have limited impact on membership privacy risks, with quantized models remaining largely as vulnerable as their full-precision counterparts. Although aggressive low-bit quantization (e.g., 2-bit) can provide meaningful privacy gains, these benefits come at the cost of substantial utility degradation. To understand the underlying causes of these observations, we systematically analyze how different quantization strategies affect model behaviors and outputs, and how these changes influence privacy vulnerability. Beyond evaluating attack risks, we further investigate whether existing privacy defenses developed for full-precision DNNs can effectively protect QNNs. Our results show that these defenses provide only limited additional privacy protection when applied to quantized models, while further amplifying utility degradation. Our study provides practical guidance for privacy-aware deployment of quantized DNNs and motivates the development of new attack and defense mechanisms for QNNs. Our code and datasets are publicly available at https://github.com/JeffffffFu/Priv-QDNN.

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