Best Paper Award

Our paper “Preserving User Privacy For Machine Learning: Local Differential Privacy or Federated Machine Learning?” has received the Best Theory Paper Award in the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19), in conjunction with IJCAI’19.

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Two Papers Accepted (SIGMOD and TKDE)

Our paper “When Query Authentication Meets Fine-Grained Access Control: A Zero-Knowledge Approach” has been accepted as a full paper by ACM SIGMOD 2018. Our paper “Authenticating Aggregate Queries over Set-Valued Data with Confidentiality” has been accepted by IEEE Transactions on Knowledge and Data Engineering.

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