报告地点：腾讯会议（301 787 208/1010）
报告简介：Increasingly individuals and companies adopt the cloud as a primary data and IT infrastructure platform. Many public cloud platforms provide cloud computing services over the outsourced data. However, while utilizing cloud services for building applications is a cost-effective solution, the potential risk of compromising sensitive information is a serious problem. Data encryption is necessary to keep sensitive information secure and private on cloud. Yet adversaries can still learn valuable information regarding encrypted data by observing access patterns.
One can completely hide the access patterns by using Oblivious RAMs (ORAMs). Numerous works have proposed different ORAM constructions, but they have never been thoroughly compared against and tested on large databases. Furthermore, there are still some major limitations while using ORAM to build an encrypted database. To that end, my research studies focus on scalable and secure data analysis in cloud and addressing the issues above. We propose a general oblivious query processing framework (OQF) for cloud databases that is efficient and practical (easy to implement and deploy) and supports concurrent query processing (i.e., concurrency within a query’s processing) with high throughput. The key idea is to integrate indices into ORAM and leverage a suite of optimization techniques (e.g., oblivious batch processing and caching). The effectiveness and efficiency of our oblivious query processing framework is demonstrated through extensive evaluations over several large datasets.
报告人介绍：常曌，西安电子科技大学副教授，2021年8月获美国犹他大学计算机专业博士学位，主要研究方向为数据安全与数据库系统。博士期间在ACM SIGMOD、VLDB、IEEE TKDE等CCF A类会议及期刊以第一作者发表数篇论文，作为核心参与者全程参与两项美国国家自然科学基金，曾在美国微软研究院、阿里巴巴达摩院等研究机构做研究实习生。