With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and "big data" related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.