Background: The increased adoption of the internet, social media, wearable devices, e-health services, and other technology-driven services in medicine and healthcare has led to the rapid generation of various types of digital data, providing a valuable data source beyond the confines of traditional clinical trials, epidemiological studies, and lab-based experiments. Methods: We provide a brief overview on the type and sources of real-world data and the common models and approaches to utilize and analyze real-world data. We discuss the challenges and opportunities of using real-world data for evidence-based decision making This review does not aim to be comprehensive or cover all aspects of the intriguing topic on RWD (from both the research and practical perspectives) but serves as a primer and provides useful sources for readers who interested in this topic. Results and Conclusions: Real-world hold great potential for generating real-world evidence for designing and conducting confirmatory trials and answering questions that may not be addressed otherwise. The voluminosity and complexity of real-world data also call for development of more appropriate, sophisticated, and innovative data processing and analysis techniques while maintaining scientific rigor in research findings, and attentions to data ethics to harness the power of real-world data.

Assessment of disclosure risk is of paramount importance in data privacy research and applications. The concept of differential privacy (DP) formalizes privacy in probabilistic terms and provides a robust concept for privacy protection. Practical applications of DP involve development of DP mechanisms to release data at a pre-specified privacy budget. In this paper, we generalize the widely used Laplace mechanism to the family of generalized Gaussian (GG) mechanism based on the $l_p$ global sensitivity of statistical queries. We explore the theoretical requirement for the GG mechanism to reach DP at prespecified privacy parameters, and investigate the connections and differences between the GG mechanism and the Exponential mechanism based on the GG distribution. We also present a lower bound on the scale parameter of the Gaussian mechanism of $(\epsilon, \delta)$ -probabilistic DP as a special case of the GG mechanism, and compare the utility of sanitized results in the tail probability and dispersion between the Gaussian and Laplace mechanisms. Lastly, we apply the GG mechanism in three experiments and compare the accuracy of sanitized results in the $l_1$ distance and Kullback-Leibler divergence, and examine the prediction power of a SVM classifier constructed with the sanitized data relative to the original results.