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邀请，Santa Clara University陆海兵博士于2016.6.8访问我校进行合作交流活动。
报告题目：Statistical Database Auditing Without Query Denial Threat
报告时间：2016年6月8日（星期三）上午10:30
报告地点：理科楼202
陆海兵个人简历：
陆海兵博士1998年入读西安交通大学，于2002年和2005年在计算数学专业分别获得学士和硕士学位，于2011年在Rutgers大学管理专业（信息科学方向）获得博士学位，曾在新加坡管理大学任研究员（2005-2006）和客座助理教授（2014），目前是Santa Clara大学的助理教授。此外，他还是期刊“International Journal of Technology, Policy and Management”中“Special issue on Big Data Analytics and Business Innovation”特刊的客座编辑。
陆海兵博士的研究兴趣主要集中在数据挖掘、隐私和安全以及优化等方向的交叉领域，已经在“IEEE Transaction on Dependable and Security Computing (TDSC)”, “INFORMS Journal on Computing (JOC)”, “Journal of Computer Security (JCS)”, “IEEE Symposium on Security and Privacy (S&P)”, “ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)”, “SIAM International Conference on Data Mining (SDM)”, “IEEE International Conference on Data Mining (ICDM)”以及“IEEE International Conference on Data Engineering (ICDE)”等期刊及会议上发表多篇论文并担任审稿人和会议成员，是一位多产学者。
报告摘要：
Statistical database auditing is the process of checking aggregate queries that are submitted in a continuous manner, to prevent inference disclosure. Compared to other data protection mechanisms, auditing has the features of flexibility and maximum information. Auditing is typically accomplished by examining responses to past queries to determine whether a new query can be answered. It has been recognized that query denials release information and can cause data disclosure. This paper proposes an auditing mechanism that is free of query denial threat and applicable to mixed types of aggregate queries, including sum, max, min, deviation, etc. The core ideas are (i) deriving the complete information leakage from each query denial and (ii) carrying the complete leaked information derived from past answered and denied queries to audit each new query. The information leakage deriving problem can be formulated as a set of parametric optimization programs, and the whole auditing process can be modeled as a series of convex optimization problems.
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