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Passively collected big data sources are increasingly used to inform critical development policy decisions in low- and middle-income countries. While prior work highlights how such approaches may reveal sensitive information, enable surveillance, and centralize power, less is known about the corresponding privacy concerns, hopes, and fears of the people directly impacted by these policies --- people sometimes referred to asexperiential experts.To understand the perspectives of experiential experts, we conducted semi-structured interviews with people living in rural villages in Togo shortly after an entirely digital cash transfer program was launched that used machine learning and mobile phone metadata to determine program eligibility. This paper documents participants' privacy concerns surrounding the introduction of big data approaches in development policy. We find that the privacy concerns of our experiential experts differ from those raised by privacy and developmentdomain experts.To facilitate a more robust and constructive account of privacy, we discuss implications for policies and designs that take seriously the privacy concerns raised by both experiential experts and domain experts. 

Abstract Personal mobility data from mobile phones and other sensors are increasingly used to inform policymaking during pandemics, natural disasters, and other humanitarian crises. However, even aggregated mobility traces can reveal private information about individual movements to potentially malicious actors. This paper develops and tests an approach for releasing private mobility data, which provides formal guarantees over the privacy of the underlying subjects. Specifically, we (1) introduce an algorithm for constructing differentially private mobility matrices and derive privacy and accuracy bounds on this algorithm; (2) use real-world data from mobile phone operators in Afghanistan and Rwanda to show how this algorithm can enable the use of private mobility data in two high-stakes policy decisions: pandemic response and the distribution of humanitarian aid; and (3) discuss practical decisions that need to be made when implementing this approach, such as how to optimally balance privacy and accuracy. Taken together, these results can help enable the responsible use of private mobility data in humanitarian response. 

Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn about populations of interest without specifically learning about these individuals. Because differential privacy allows us to quantify cumulative privacy loss, these differentially private systems will, for the first time, allow us to measure and compare the total privacy loss due to these personal data-intensive activities. Appropriately leveraged, this could be a watershed moment for privacy. Like other technologies and techniques that allow for a range of instantiations, implementation details matter. When meaningfully implemented, differential privacy supports deep data-driven insights with minimal worst-case privacy loss. When not meaningfully implemented, differential privacy delivers privacy mostly in name. Using differential privacy to maximize learning while providing a meaningful degree of privacy requires judicious choices with respect to the privacy parameter epsilon, among other factors. However, there is little understanding of what is the optimal value of epsilon for a given system or classes of systems/purposes/data etc. or how to go about figuring it out. To understand current differential privacy implementations and how organizations make these key choices in practice, we conducted interviews with practitioners to learn from their experiences of implementing differential privacy. We found no clear consensus on how to choose epsilon, nor is there agreement on how to approach this and other key implementation decisions. Given the importance of these implementation details there is a need for shared learning amongst the differential privacy community. To serve these purposes, we propose the creation of the Epsilon Registry—a publicly available communal body of knowledge about differential privacy implementations that can be used by various stakeholders to drive the identification and adoption of judicious differentially private implementations.