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Machine learning on personal and sensitive data raises serious privacy concerns and creates potential for inadvertent information leakage (e.g., extraction of private messages or images from generative models). However, incorporating analysis of such data in decision making can benefit individuals and society at large (e.g., in healthcare). To strike a balance between these two conflicting objectives, one must ensure that data analysis with strong confidentiality guarantees is deployed and securely implemented. Differential privacy (DP) is emerging as a leading framework for analyzing data while maintaining mathematical privacy guarantees. Although it has seen some real-world deployment (e.g., by Apple, Microsoft, and Google), such instances remain limited and are often constrained to specific scenarios. Why? In this talk, I argue that part of the challenge lies in the assumptions DP makes about its deployment environment. By examining several DP systems and their assumptions, I demonstrate how private information can be extracted using, for example, side-channel information or the ability to rewind system’s state. I then give an overview of efficient algorithms and protocols to realize these assumptions and ensure secure deployment of differential privacy.