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Title: Privacy Preserving Machine Learning: A Theoretically Sound Approach
Speaker: Wang Liwei(Beijing University)
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.