Learning analytics research presents challenges for researchers embracing the principles of open science. Protecting student privacy is paramount, but progress in increasing scientific understanding and improving educational outcomes depends upon open, scalable and replicable research. Findings have repeatedly been shown to be contextually dependent on personal and demographic variables, so how can we use this data in a manner that is ethical and secure for all involved? This paper presents ongoing work on the MOOC Replication Framework (MORF), a big data repository and analysis environment for Massive Open Online Courses (MOOCs). We discuss MORF's approach to protecting student privacy, which allows researchers to use data without having direct access. Through an open API, documentation and tightly controlled outputs, this framework provides researchers with the opportunity to perform secure, scalable research and facilitates collaboration, replication, and novel research. We also highlight ways in which MORF represents a solution template to issues surrounding privacy and security in the age of big data in education and key challenges still to be tackled.