地点：网安院会议室（信息喽C312）

时间：2020年7月30日（周四）上午10：00

个人简介:

刘健，浙江大学百人计划研究员，博士生导师。2019年 11月加入浙江大学网络空间安全学院。2018年7月 获得芬兰阿尔托学博士学位，并于同年加入加州大学伯克利分校担任博士后研究员。其研究领域涵盖应用密码学、分布式系统、区块链、人工智能。致力于构建可证明安全的、易用的、可部署的系统应用。

报告摘要:

In the last decade, there has been a move towards making traditional IT services follow a cloud-assisted services paradigm. This has triggered previously local services to be moved to a cloud-assisted setting to reap the advantages of the cloud-assisted paradigm that can work with simple client- side functionality ("thin clients"). Examples of such services are cloud storage, cloud - assisted malware checking and ”machine learning as a service" (ML aas). Despite their benefits, these kinds of services put users' privacy at risk since the data stored in the cloud and/or the requests submitted to the cloud may contain sensitive information. On the other hand, unless carefully designed, this service paradigm may nonetheless fail to protect the confidentiality of service providers' business assets (e.g., malware databases or machine learning models) against malicious users.

This talk shows how to leverage cryptographic technologies and trusted execution environments to design cloud assisted services such that end users can protect their privacy, and if needed, service providers can ensure that their security/ privacy requirements are not violated. We provide a general definition for privacy-preserving cloud-assisted services, investigate the privacy issues in three cloud-assisted services: lookup service, prediction service and storage service, and propose solutions on how to make them privacy- preserving.