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KAIST Graduate  School of Information Security

공지사항

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[종료] Texas Austin 이상민 박사과정 세미나(2014.01.07.화 16시~)
이름 : admin | 작성일 : 2014.01.03 10:23 | 조회수 : 7744

정보보호대학원에서는 아래와 같은 주제로 UT Austin 이상민 박사과정의 세미나가 있을 예정입니다.

관심 있으신 정보보호대학원 교수님, 학생분들의 많은 참여 부탁드립니다.


================================== 아 래 ==========================================


연사: 이상민 (Texas Austin)

일시: 1월 7일(화) 16:00 ~ 17:00

장소: N1 113호


Title: Towards a platform for privacy-preserving apps


Abstract:


App platforms, such as smartphones, tablets, browsers and even wearable devices, are becoming prevalent because of various functionality provided by apps running those platforms. To provide useful functionality, apps often need to access users' sensitive information such as their locations, behaviors in the past, credentials for accounts, etc. Unfortunately, once users grant apps access to their data, there is no control on what the apps can do with the given data. As a result, in order to use app functionality, users are forced to trust app publishers who are often obscure and also potentially malicious. To address this problem, we present a new platform architecture that enforces privacy even with untrusted apps. With the platform-enforced privacy, users can download and use apps on their devices without worrying about who wrote the apps or what the apps will do with their data. Our approach is based on sandboxing mechanism that confines apps to prevent them from leaking users' private data. On top of the confinement, we provide several controlled communication channels allowing apps to support diverse functionality.The key challenge in this approach is: Can we provide sufficient confinement for privacy and yet allow the confined apps to support useful functionality? We address this challenge by introducing several novel techniques that balance apps' functional needs against their users' privacy, largely preserving both. In particular, by composing different types of sandboxes in a novel way, our platform can support privacy-preserving collaborations among users. Moreover, we introduce new techniques that leverage differential privacy to allow the app publishers to collect aggregate statistics about behavior of users, which enables more useful and necessary functionality including advertisement and debugging.


 

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