As mobile health (mHealth) interventions have the potential to acquire a dominant role in safety-net healthcare settings, there are many challenges to data privacy that need to be considered. Users with marginalized backgrounds have a greater risk of experiencing more detrimental consequences of privacy and security breaches to mHealth apps that collect and offer critical, sensitive, and private health information. However, there is a lack of evidence base on the privacy and security knowledge, attitudes, and apprehensions among users from vulnerable groups. The Digital Health Equity and Access Lab (dHEAL), in the UC Berkeley School of Social Welfare, has developed an adaptive text-messaging intervention to encourage physical activity among low-income ethnic minority patients with comorbid depression and diabetes. The intervention is streamed through a smartphone app that uses machine learning to predict which categories of text messages are most effective, based on participants’ contextual variables. This qualitative study seeks to understand patients’ knowledge of and attitudes toward the privacy and security of their own data, including having their step count and location data tracked by a smartphone app. The study will focus on their understandings of data security and privacy and the risks and benefits of using a digital health self-management intervention. The aims of this study are to explore the cybersecurity implications of mobile applications that collect the personal data of underserved individuals and to create a framework for researchers to protect the privacy of vulnerable research subjects.