In order to reduce physical unclonable fixnction （PUF） response instability and imbalance caused by the metastability and the bias of arbiter, this paper uses an improved balanced D flip-plop （DFF） based on the un...In order to reduce physical unclonable fixnction （PUF） response instability and imbalance caused by the metastability and the bias of arbiter, this paper uses an improved balanced D flip-plop （DFF） based on the unbalanced DFF to reduce the bias in response output and enhances the security of PUF by adopting two balanced DFFs in series. The experimental results show that two cascaded balanced DFFs improve the stability of the DFF, and the output of two balanced DFFs is more reliable. The entropy of output is fixed at 98.7%.展开更多
User preference data broadly collected from e-commerce platforms have benefits to improve the user’s experience of individual purchasing recommendation by data mining and analyzing,which may bring users the risk of p...User preference data broadly collected from e-commerce platforms have benefits to improve the user’s experience of individual purchasing recommendation by data mining and analyzing,which may bring users the risk of privacy disclosure.In this paper,we explore the problem of differential private top-k items based on least mean square.Specifically,we consider the balance between utility and privacy level of released data and improve the precision of top-k based on post-processing.We show that our algorithm can achieve differential privacy over streaming data collected and published periodically by server provider.We evaluate our algorithm with three real datasets,and the experimental results show that the precision of our method reaches 85%with strong privacy protection,which outperforms the Kalman filter-based existing methods.展开更多
基金Supported by the National Natural Science Foundation of China(41371402)the Fundamental Research Funds for the Central Universities(2015211020201)
文摘In order to reduce physical unclonable fixnction （PUF） response instability and imbalance caused by the metastability and the bias of arbiter, this paper uses an improved balanced D flip-plop （DFF） based on the unbalanced DFF to reduce the bias in response output and enhances the security of PUF by adopting two balanced DFFs in series. The experimental results show that two cascaded balanced DFFs improve the stability of the DFF, and the output of two balanced DFFs is more reliable. The entropy of output is fixed at 98.7%.
基金Supported by the National Natural Science Foundation of China(61772562)Major Projects of Technical Innovation of Hubei Province(CXZD2018000035)+2 种基金the Applied Basic Research Project of Wuhan(2017060201010162)the Fundamental Research Funds for the Central Universities(2042017gf0038,YZZ18002)the Provincial Teaching Research Project of Higher Education in Hubei Province(2017523)
文摘User preference data broadly collected from e-commerce platforms have benefits to improve the user’s experience of individual purchasing recommendation by data mining and analyzing,which may bring users the risk of privacy disclosure.In this paper,we explore the problem of differential private top-k items based on least mean square.Specifically,we consider the balance between utility and privacy level of released data and improve the precision of top-k based on post-processing.We show that our algorithm can achieve differential privacy over streaming data collected and published periodically by server provider.We evaluate our algorithm with three real datasets,and the experimental results show that the precision of our method reaches 85%with strong privacy protection,which outperforms the Kalman filter-based existing methods.
