  Homomorphic encryption, secure multi-party computation, and differential
privacy are part of an emerging class of Privacy Enhancing Technologies which
share a common promise: to preserve privacy whilst also obtaining the benefits
of computational analysis. Due to their relative novelty, complexity, and
opacity, these technologies provoke a variety of novel questions for design and
governance. We interviewed researchers, developers, industry leaders,
policymakers, and designers involved in their deployment to explore
motivations, expectations, perceived opportunities and barriers to adoption.
This provided insight into several pertinent challenges facing the adoption of
these technologies, including: how they might make a nebulous concept like
privacy computationally tractable; how to make them more usable by developers;
and how they could be explained and made accountable to stakeholders and wider
society. We conclude with implications for the development, deployment, and
responsible governance of these privacy-preserving computation techniques.
