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Seminar abstract

Privacy Preserving Data Releasing for Smooth Queries


Abstract: Privacy is an important concern in the big data era. The potential benefit for scientific study from these data is huge. But how can the database holder release sensitive data while preserving individual privacy? In this talk I will review a recent rigorous definition of privacy: differential privacy. Differential privacy guarantees that there is almost nothing new can be learned from a database if an individual contributes her data compared to that can be learned from the same database except her data is not in; and thus there is no harm for an individual to contribute her data. Previous algorithms on differential privacy are usually inefficient. In fact, it can be shown that answering general queries while preserving differential privacy is computationally hard. I will give a very efficient algorithm (sublinear time in many parametric settings), which can answer a broad class of queries of high practical interest.

Biography: 北京大学信息科学技术学院教授。于清华大学电子工程系获本科和硕士学位,北京大学数学学院获博士学位。自2005年起在北京大学信息学院任教。主要研究兴趣为机器学习理论。在机器学习顶级会议NIPS, COLT, ICML和顶级期刊JMLR, IEEE Trans. PAMI发表论文多篇。其中2008年发表于机器学习理论最高会议COLT的论文On the Margin Explanation of Boosting Algorithms是中国大陆学者在该会议上的首篇论文。2010年入选AI’s 10 to Watch,是首位获得该奖项的亚洲学者。2012年获得国家自然科学基金优秀青年基金;入选新世纪优秀人才。目前任中国计算机学会模式识别与人工智能专委会委员。担任Journal of Computer Science and Technology (JCST)等期刊编委。
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