For the developers of next-generation education technology (EdTech), the use of Learning Analytics (LA) is a key competitive advantage as the use of some form of LA in EdTech is fast becoming ubiquitous. At its core LA involves the use of Artificial Intelligence and Analytics on the data generated by technology-mediated learning to gain insights into how students learn, especially for large cohorts, which was unthinkable only a few decades ago. This LA growth-spurt coincides with a growing global "Ethical AI" movement focussed on resolving questions of personal agency, freedoms, and privacy in relation to AI and Analytics. At this time, there is a significant lack of actionable information and supporting technologies, which would enable the goals of these two communities to be aligned. This paper describes a collaborative research project that seeks to overcome the technical and procedural challenges of running a data-driven collaborative research project within an agreed set of privacy and ethics boundaries. The result is a reference architecture for ethical research collaboration and a framework, or roadmap, for privacy-preserving analytics which will contribute to the goals of an ethical application of learning analytics methods.