SAILING讲坛第九期【隐私保护图相似计算】将于2023年12月27日下午2点在哈尔滨工业大学（深圳）T2-404举办。本期将有特邀嘉宾广州大学人工智能研究院彭云教授给我们做精彩报告。
演讲嘉宾简介：
彭云，博士，教授，2013年博士毕业于香港浸会大学，2022年加入广州大学人工智能研究院。曾在香港浸会大学担任研究助理教授，联想集团（香港）担任高级研发经理、高级数据科学家，新加坡南洋理工大学担任Research Fellow等。主要从事面向新型应用的大数据管理，机器学习，隐私保护等领域的教学与研究工作。长期担任领域顶级学术期刊和会议编委。近年来，在大数据智能及应用领域主持国家重点研发计划课题和国家自然科学基金2项，省自然科学基金2项，在SIGMOD、PVLDB、ICDE、INFOCOM、IJCAI、TKDE、TMC等CCF A类会议和期刊上发表论文30余篇，出版教材1部。
讲座摘要：
Privacy preserving graph similarity search
Graph similarity search is a fundamental problem in graph data analysis. However, preserving the privacy of graphs during the similarity search is challenging. First, graph similarity search is NP-hard. The pruning techniques, which is critical for the efficiency in non-private graph similarity search methods could leak the privacy of graphs. Second, due to the regulations or business concerns, the owners of graph data may not be able to share their data directly. They have to collaborate in a federated manner, where the original data does not leave each data owner. However, the network access pattern during the collaborated searching could leak the privacy of graph data. In the past years, we have investigated several techniques to address the two technical challenges, which will be presented in this sharing.
