CRII: SaTC: Re-Envisioning Contextual Services and Mobile Privacy in the Era of Deep Learning

深度学习（DL）支持的个性化有望从根本上改变人们的生活、工作和旅行方式，但对人们的个人隐私构成了很高的风险。该项目将通过开发解决方案来解决DL驱动的上下文移动的服务中出现的隐私风险，该解决方案促进个人信息的使用，同时保持用户对信息使用的明确控制。开发的学习方法将使学习从移动的设备的方式足够灵活，使当前和未来的DL供电的上下文服务，同时保持明确的用户控制如何使用该信息由第三方服务providers.This研究将设计和实现PADbrown，一个隐私感知深度学习上下文知识引擎。PADEXP在沙箱环境中对用户的个人数据执行DL计算，同时执行轻量级静态和运行时分析，以确保移动的应用程序符合用户的隐私政策。PADBACK的设计探讨了隐私保护，通信成本，系统开销和服务质量之间的权衡，提供了不同的可证明的隐私和效率的特点，为广泛的上下文移动的服务的解决方案。 欲了解更多信息，请访问项目网站：http://x-machine.github.io/project/padlock

Deep Learning (DL)-powered personalization holds great promise to fundamentally transform the way people live, work and travel, but poses high risk to people's individual privacy. This project will address the privacy risks arising in DL-powered contextual mobile services by developing solutions that facilitate the use of personal information while maintaining explicit user control over use of the information. The developed learning methods will enable learning from mobile devices in a manner flexible enough to enable current and future DL-powered contextual services, while maintaining explicit user control over how that information is used by third-party service providers.This research will design and implement PADLOCK, a Privacy-Aware Deep Learning Of Contextual Knowledge engine. PADLOCK executes DL computation over users' personal data in a sandbox environment, while performing lightweight static and runtime analysis to ensure that mobile apps comply with users' privacy policies. The design of PADLOCK explores the tradeoff among privacy protection, communication cost, system overhead and service quality, providing solutions with different provable privacy and efficiency features for a wide range of contextual mobile services. For further information see the project web site at: http://x-machine.github.io/project/padlock