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pro vyhledávání: '"Chen, Joann Qiongna"'
As the utilization of network traces for the network measurement research becomes increasingly prevalent, concerns regarding privacy leakage from network traces have garnered the public's attention. To safeguard network traces, researchers have propo
Externí odkaz:
http://arxiv.org/abs/2409.05249
Training a machine learning model with data following a meaningful order, i.e., from easy to hard, has been proven to be effective in accelerating the training process and achieving better model performance. The key enabling technique is curriculum l
Externí odkaz:
http://arxiv.org/abs/2310.10124
Autor:
Wang, Tianhao, Chen, Joann Qiongna, Zhang, Zhikun, Su, Dong, Cheng, Yueqiang, Li, Zhou, Li, Ninghui, Jha, Somesh
In this paper, we study the problem of publishing a stream of real-valued data satisfying differential privacy (DP). One major challenge is that the maximal possible value can be quite large; thus it is necessary to estimate a threshold so that numbe
Externí odkaz:
http://arxiv.org/abs/2005.11753