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.
翻译:基因加密、安全的多党计算和不同的隐私是新兴的隐私增强技术类别的一部分,这些技术有着共同的希望:既保护隐私,又获得计算分析的好处。这些技术由于相对新颖、复杂和不透明,在设计和治理方面引起了各种新颖的问题。我们采访了研究人员、开发人员、工业领导人、决策者和设计者,他们参与运用这些技术以探寻动机、期望、认知的机会和采用障碍。这为采用这些技术所面临的若干相关挑战提供了深入的了解,包括:它们如何使隐私权等模糊概念在计算上具有可移动性;如何使开发者更能加以利用;如何解释这些技术,并对利益攸关方和更广泛的社会负责。我们的结论是这些隐私保护计算技术的开发、部署和负责任治理所涉及的问题。