Abstract: This disclosure belongs to the technical field of distributed machine learning, and specifically relates to an adaptive privacy-protection distributed learning method and device based on attenuated noise perturbation. The method includes: acquiring a local gradient of a node according to a sample gradient after node clipping, wherein a clipping threshold of the node decreases with the increase of iteration rounds; injecting Gaussian noise into the local gradient, wherein the intensity of the Gaussian noise is stepwise attenuated with the increase of iteration rounds; aggregating the local gradient of the node after injection of the Gaussian noise in each iteration round, and using the aggregated gradient to update local model parameters, and broadcasting the updated local model parameters to adjacent nodes for parameter updating; and then aggregating the updated model parameters of the adjacent nodes for the next iteration.

Abstract: Disclosed is a method for detecting a data stealthy attack on a networked system with differential privacy protection, which falls within the technical field of information security. The method includes: firstly, modeling a networked system and designing an attack detection scheme based on system noise parameters; then, designing an optimal data stealthy attack scheme for an attacker according to known system information; and then, determining a moment of adding a privacy noise through a privacy noise scheduling scheme while ensuring privacy of sensitive data on the networked system, and achieving an optimal system control performance. Based on the above design, a possible data stealthy attack may be effectively detected on the basis of protecting the system data privacy and ensuring the optimal system performance.

Abstract: Disclosed is a method for detecting a data stealthy attack on a networked system with differential privacy protection, which falls within the technical field of information security. The method includes: firstly, modeling a networked system and designing an attack detection scheme based on system noise parameters; then, designing an optimal data stealthy attack scheme for an attacker according to known system information; and then, determining a moment of adding a privacy noise through a privacy noise scheduling scheme while ensuring privacy of sensitive data on the networked system, and achieving an optimal system control performance. Based on the above design, a possible data stealthy attack may be effectively detected on the basis of protecting the system data privacy and ensuring the optimal system performance.

Abstract: A new mutant of D-amino acid transaminase, based on supercomputing-assisted technology, belongs to the fields of computational biology, computer-aided design, and enzyme engineering technology, and specifically relates to mutants of D-amino acid transaminase obtained based on supercomputing-assisted technology and applications thereof. Compared to the wild-type enzyme, the aforementioned mutant of D-amino acid transaminase exhibits a half-life (t1/2) of over 12 hours at 40° C., while it is only 8.8 minutes for the wild-type D-amino acid transaminase. The half-inactivation temperature (T5015) of the mutant is 45.3° C., approximately 5.4° C. higher than that of the wild-type D-amino acid transaminase. This substantially enhances its thermal stability, enzymatic activity, etc., effectively broadening its application fields and scope. The mutant has a wide range of industrial applications and thus holds substantial practical value.

Abstract: A new mutant of D-amino acid transaminase, based on supercomputing-assisted technology, belongs to the fields of computational biology, computer-aided design, and enzyme engineering technology, and specifically relates to mutants of D-amino acid transaminase obtained based on supercomputing-assisted technology and applications thereof. Compared to the wild-type enzyme, the aforementioned mutant of D-amino acid transaminase exhibits a half-life (t1/2) of over 12 hours at 40° C., while it is only 8.8 minutes for the wild-type D-amino acid transaminase. The half-inactivation temperature (T505) of the mutant is 45.3° C., approximately 5.4° C. higher than that of the wild-type D-amino acid transaminase. This substantially enhances its thermal stability, enzymatic activity, etc., effectively broadening its application fields and scope. The mutant has a wide range of industrial applications and thus holds substantial practical value.

Abstract: A method for controlling a self-service vending device to be safely opened, a server and a self-service vending system are provided.