32 results about "Random response" patented technology

The present application belongs to the field of privacy communication and communication security assurance, and discloses a two-way secure communication method based on a random response mechanism. The secure communication method checks whether the receiving user and the sending user are legal by sending and receiving pulse check signals between the sender and the receiver. It is a two-way secure communication method in which the sender and the receiver authenticate and approve each other, and the legality of the other party can be determined synchronously. Even if an eavesdropper or a forger accidentally passes an identity authentication, the communication security has enough fault tolerance space, and it is difficult for an illegal user to pass the authentication continuously for many times, so that the sender or the receiver will terminate the communication, thereby ensuring the security of information communication.

This invention discloses a robust design method for gas turbine performance based on multi-source stochastic response coordination, relating to the field of gas turbine performance design. The method sequentially performs three types of stochastic influence analysis: design model, manufacturing, and usage process, constructing probability density functions for core performance parameters respectively. Based on the task phase, it determines the risk levels and weights of various stochastic factors, fusing them to generate a comprehensive probability density function. With the goal of minimizing the dispersion and optimizing the expected value of the comprehensive probability density function, it combines the constraints of component thermodynamic state parameters to solve the optimization model, obtaining the optimal combination of performance parameters. This invention achieves multi-source stochastic coordinated quantification and characterization, improving the robustness of gas turbine performance, solving the problems of conservative design and poor adaptability to operating conditions in traditional methods, and enhancing its operational safety and reliability.

The application discloses a user preference prediction method for sensitive behavior data protection and relates to the technical field of big data analysis. The application introduces the localized differential privacy of random response in the construction of dynamic characteristics of preferences, protects the security of potential feature data of the preferences, fuses a time decay factor in the process of learning dynamic characteristics, optimizes the dependency relationship between features in different periods, and realizes user dynamic preference prediction under dynamic change and sensitive behavior protection, so that the application realizes the construction of a privacy protection mechanism for sensitive behavior of users and retains the availability of data, and improves the accuracy of future preference prediction.

The invention particularly relates to random response analysis of a wake flow type hybrid energy collector under fluctuating wind excitation, which comprises the following steps: on the basis of a simplified model that a static upstream bluff body is arranged in front of a main bluff body, and periodic vortex shedding wake flow force is utilized to excite vibration, establishing an equivalent electromechanical coupling dynamic model; a random phase is introduced into wake-induced vibration external excitation to represent random fluctuation of wake force under the fluctuating wind; after dimensionless and parameter scale re-calibration are carried out on the model, a multi-scale method is adopted to derive an amplitude-phase slow modulation equation set and first-order asymptotic analysis response expressions of displacement, voltage and current; further linearizing the slow-varying equation under a steady-state condition, solving a second-order statistic of amplitude and phase by combining a moment method and an Ischi differential rule, and determining steady-state moments of the output voltage and the output current and average output power according to the second-order statistic; and the output performance rapid evaluation and parameter design support of the wake flow type hybrid energy collector under random excitation are realized.

The invention relates to the technical field of data privacy protection, and provides a sketch-based low-frequency item frequency estimation method under local differential privacy, which comprises the following steps that: a user constructs a multi-layer sketch structure based on a local data set, and disturbs column coordinates of each layer of sketch by applying a generalized random response mechanism to meet local differential privacy constraints; the server receives disturbed row coordinate and column coordinate pairs uploaded by all users, aggregates the disturbed row coordinate and column coordinate pairs into a global multilayer sketch to construct a global multilayer sketch matrix, calibrates observation count of each layer of the global multilayer sketch matrix, estimates the real frequency of each layer of the global multilayer sketch matrix, and calculates the real frequency of each layer of the global multilayer sketch matrix based on the calibrated frequency estimation of each layer. And the query item is subjected to joint calculation to obtain global frequency estimation of the query item. According to the method, by introducing a local differential privacy mechanism and optimizing a sketch structure, the effect of remarkably improving the frequency estimation precision of the low-frequency data item while strictly protecting the individual privacy of the user is achieved.

This invention provides a local differential privacy collaborative sampling data collection method and system resistant to poisoning attacks, belonging to the field of information security and privacy protection technology. The scheme compares the single-dimensional probability distribution reported by the user terminal with the median probability distribution to identify and eliminate abnormal reported data, thereby effectively suppressing data poisoning attacks initiated by malicious user terminals. Subsequently, under the constraint of local differential privacy, combined with a generalized random response mechanism and a collaborative sampling strategy, multidimensional data is sampled differentially to achieve more accurate frequency estimation. The scheme of this invention improves the privacy protection strength and the accuracy of statistical results in multidimensional data collection without relying on a trusted third party.

This invention discloses a structural topology optimization design method considering the uncertainty of multi-peak loads, belonging to the field of uncertain structural optimization design. It mainly includes three parts: establishing a multi-peak load uncertainty model, solving for the random response, and solving for the optimization formula. This invention describes load uncertainty using a Gaussian mixture model, solves for the Gaussian mixture model coefficients based on the EM algorithm, and establishes a multi-peak load distribution probability model. By decorrelating random variables, sparse grid technology is used to solve for the mean, standard deviation, and sensitivity of the response, thereby solving for the topology optimization model considering the multi-peak load uncertainty. This method addresses the problem of low structural product reliability that may occur in structural designs considering multi-peak load uncertainty by establishing an accurate probabilistic model of multi-peak load uncertainty. This method is simple and easy to implement, readily applicable in engineering, and can improve the design efficiency of structural designers considering complex load conditions.

The invention relates to privacy protection, in particular to an AMI network-oriented portrait perception adaptive privacy protection method, which comprises the following steps: quantifying the importance of users through a fuzzy comprehensive evaluation algorithm, and providing different protection levels for different users according to the importance, the higher the importance is, the higher the protection level is; distributing a privacy budget for each user according to the protection level of the user; based on the distribution privacy budget, adopting a square wave mechanism to disturb data of a numerical value type in the original data of the user; on the basis of distribution of privacy budget, calculating a theoretical mean square error of generalized random response and optimal unary coding, and selecting a mode with a relatively small error to disturb discrete type data in the original data of the user; and after the disturbed data and the privacy budget thereof are uploaded to the server, the server recovers the user data with the same privacy budget by adopting a hierarchical EMS algorithm. According to the method, the non-uniform transfer matrix and the virtual grid projection are constructed, so that seamless fusion of different privacy budgets and different discrete granularity data is realized.

To provide an in-store showcase control system configured to automatically set a case address for each of a plurality of showcases installed in a store, without requiring an operator to randomly set serial numbers to the showcases in advance prior to shipping the showcases to the store.SOLUTION: In an in-store showcase control system 100, a control apparatus 20 is configured to transmit, to each of a plurality of showcases 10, an address setting command 31 to set a case address 30 for identifying each of the showcases 10, on the basis of random response delay time information 40 transmitted from each of the showcases 10 at timings based on response delay time of the response delay time information 40 generated randomly in each of the showcases 10.SELECTED DRAWING: Figure 3

The application discloses a kind of based on adaptive high-dimensional data local differential privacy protection method, using rough set theory and mutual information, the data of different relevance and sensitivity are clustered grouping, different grouping is used different random response probability, permanent random response and temporary random response are carried out.Local differential privacy processing data, using SNE method for dimension reduction and adaptive sampling gradient optimization in server end, with low-dimensional data joint probability distribution approximates the joint distribution of all attributes, according to the sampling strategy and the approximate joint distribution constitutes release dataset.The application solves the problem of high-dimensional data with dimension disaster and local differential privacy combination and maximum degree maintaining data relevance, and more reasonably allocates privacy budget according to the different data sensitivity to some extent, improves data availability and reduces communication cost.

This invention discloses a local differential privacy trajectory publishing algorithm oriented towards regions of interest, comprising the following steps: Step 1, extracting a set of safe regions from the original user trajectory; Step 2, traversing each trajectory point in the original user trajectory in chronological order, and further dividing the trajectory points falling into the safe regions to obtain discrete candidate domains and nearest candidate points; Step 3, performing distance-layered restricted random response within the discrete candidate domains to generate perturbation points, wherein the perturbation points and trajectory points not falling into any safe regions constitute the published trajectory. This invention features a finer-grained trade-off between privacy strength and trajectory availability.

This invention belongs to the field of power system demand-side management technology and discloses a probabilistic assessment method for load adjustability potential considering dual uncertainties. The method includes: constructing adjustability potential models for five load types: air conditioning, cold chain, energy storage, electric vehicles, and data centers; determining the random parameters of external influences on each load and quantifying their probability distribution based on historical data; introducing random execution variables for each load type to describe the uncertainty of execution behavior with probability distributions, thus obtaining a more realistic adjustability potential; performing random sampling calculations on the above models and parameters using the Monte Carlo method to aggregate and generate a total adjustability potential sample; and performing fitting analysis on the sample to obtain the probability function of the system's adjustability potential and conduct multi-dimensional evaluation. This invention achieves the quantification of load adjustability potential uncertainty through a three-layer probabilistic modeling of "physical behavior - random influence - random response," providing decision support for optimized power grid operation.

The invention discloses a multivariable time point process analysis method based on two-dimensional local differential privacy, and relates to the technical field of federated learning and privacy protection. The method comprises the following steps: setting a total privacy budget on a client side and distributing the budget as a time and type dimension budget; a delta t-SAHRP-LDP order-preserving perturbation mechanism is adopted in the time dimension, interval scale adaptive noise adding, minimum right lower bound constraint and dynamic anchor point resetting are combined, and a time sequence after perturbation is generated; and generating a disturbed type sequence in the type dimension by adopting a random response mechanism. A client uploads an intermediate representation and an alignment scale based on disturbance data, and a server establishes a cross-end alignment and neighborhood relationship only according to the intermediate representation and the alignment scale, generates extension and neighborhood context features and returns the extension and neighborhood context features. And the client performs prediction and training in combination with the local real representation and the cross-end context. According to the method, on the premise of not exposing an original sequence, the leakage risk of sensitive time rhythm and type distribution is effectively reduced, and meanwhile, the accuracy of event prediction is improved by utilizing cross-client collaborative information.

The invention particularly relates to a random response analysis method for a piezoelectric-electromagnetic hybrid energy collector under pulsating wind excitation, and the method comprises the steps: building a mechanical vibration equation containing a galloping aerodynamic item and a time-delay feedback control item, and enabling the mechanical vibration equation to be combined with a piezoelectric voltage state equation and an electromagnetic current state equation to form an initial electromechanical coupling equation set; therefore, the mechanical end dynamics and the energy conversion effect of the two branches are simultaneously represented in the same model. Introducing the dimensionless fluctuating wind speed into galloping aerodynamic force and performing equivalent modeling by using multiplicative noise so as to explicitly map the fluctuating wind speed into a state-related random excitation item; voltage and current variables are equivalent to algebraic terms of displacement and speed by adopting harmonic transformation, and an equivalent decoupling system is obtained, so that model variable transformation is realized, and the analytical solution efficiency is improved; and finally, a steady-state probability density function is obtained based on energy envelope random averaging, and a random response analysis result is calculated according to the steady-state probability density function, so that a reliable basis is provided for evaluation of control parameters and energy harvesting performance.

The application discloses a kind of distributed flow table item effective time dynamic hopping implementation methods, including distributed flow table item effective time dynamic hopping definition, implementation, and balance security, performance and resource consumption three features such as characteristic.This method, flow table item in flow table existence time is regarded as privacy, through the ladder random response mechanism of differential privacy in SDN forwarding equipment local flow table item in flow table existence time is dynamically disturbed, forms dynamic mobile flow table item attack surface, makes low-speed flow table overflow attack unable to synchronize flow table item attack surface, to the attack has realized effective active defense.The method does not need to upgrade existing SDN interface protocol and controller software, does not need to introduce trusted third party, while considering network security, guarantee network performance and expansibility, especially suitable for various types of need to support high security and large concurrent user, and has special requirements to delay and expansibility, guarantee its service quality and user experience.

The invention discloses an anti-seismic reliability analysis method, device and system for a self-resetting assembled pier, and a storage medium. The method comprises the following steps: step 1, determining a basic random variable of a structure; 2, selecting a representative sample, and calculating an assignment probability of a probability subspace; step 3, structural random response analysis; 4, carrying out structure hysteresis analysis; 5, calculating a damage index and an evolution speed of each sample; step 6, defining a damage criterion of the self-resetting segment prefabricated assembled pier; 7, solving a generalized probability density evolution equation with an absorption boundary; step 8, obtaining a'residual 'probability density function; and step 9, evaluating the time-varying anti-seismic reliability of the self-resetting segment prefabricated assembled pier. By adopting the technical scheme of the invention, scientific evaluation of the anti-seismic safety performance of the self-resetting segment prefabricated assembled pier under the consideration of the influence of material randomness is realized.

The present application relates to a kind of medical blockchain data sharing methods based on double mass threshold and federal learning, belong to computer technology field.The method is:1, publishing node publishes task and is built committee;2, participating node downloads global model parameter from blockchain, carries out local model training in combination with random response and differential privacy technology;3, the node of completion training uploads local model parameter to blockchain, committee election mining node downloads all transactions from blockchain and calculates quality, only the node passing through quality threshold can participate in model aggregation and calculate contribution value as its aggregation weight;4, mining node updates the reputation information of each node and is packed into new block with global model parameter;5, other committee node verifies new block, after verification, new block is chained, while adjusting committee composition.The present application can resist the problem of poisoning attack existing in federal learning, while protecting data privacy and reducing the influence of noise on model performance.

The application discloses a non-smooth system random response calculation method based on improved generalized cell mapping. The method firstly establishes a random differential equation of a target non-smooth system and defines a collision condition; then selects a region of interest on a collision constraint surface, discretizes the region of interest into a cell state space and establishes a mapping relationship with an original space; then constructs a collision-to-collision mapping on the collision constraint surface, and establishes a one-step transition probability matrix of the generalized cell mapping method; finally, based on Markov chain theory, transient and steady-state probability distributions on the cell state space are calculated, and are restored to the original continuous state space. The application can accurately calculate transient and steady-state responses of a non-smooth system, and is suitable for long-term response analysis.

The application provides a structure probability risk assessment method and system based on SRSMBRA and Copula-MCM, and the method comprises the following steps: improving the probability matching point of a classical random response surface method, obtaining undetermined coefficients of a Hermite expansion by a random response surface method SRSMBRA based on regression analysis, taking the probability matching point method as a breakthrough point, and constructing a limit state equation of a structure; based on the limit state equation of the structure, constructing a joint probability density function of multi-dimensional random variables of the structure by means of a Copula function and a Vine model, obtaining a failure probability when there is a correlation between the multi-dimensional random variables of the structure based on a structure probability risk assessment method of Copula-MCM; and integrating the structure probability risk assessment of SRSMBRA and Copula-MCM by means of Rosenblatt transformation, obtaining the failure probability by a Copula-SRSMBRA risk assessment method by using a moment method or a Monte Carlo method, and performing efficient and feasible structure probability risk assessment of an engine life-limited part by the above method.

The application provides a static power distribution network system observability evaluation method based on a random response surface method. On the basis of using the random response surface method to perform observability analysis on the static power distribution network system, the basis for judging whether the power distribution network system is observable is that: the system is observable only when the observability coefficient matrix has n linearly independent column vectors, and the i th row and the n+i th row cannot be zero vectors at the same time. Further, an index is constructed for performing observability analysis on the static power distribution network system under the scenes of noisy measurement data, missing measurement data and delayed measurement data, so as to ensure the normal and stable operation of the power distribution network system.

The application discloses a personalized differential privacy protection method for wedge-shaped counting of graph data and belongs to the technical field of data privacy protection. The application fuses a double privacy amplification mechanism of sampling amplification and shuffle amplification, constructs a two-stage framework of personalized sampling and shuffle counting, and relates to an independent third party, a local client, a shuffler and a data collector; node-level non-uniform sampling is completed through node sensitive level division, personalized privacy budget allocation and optimal sampling threshold solving, and then unbiased estimation of a wedge-shaped total number is realized through local random response disturbance, shuffler anonymization and statistical debiasing. The application is free from dependence on a trusted data collector, takes into account node personalized privacy demand and counting precision, reduces noise overhead, and is suitable for graph data privacy protection scenes such as social networks and financial transactions.

The application discloses an improved random response surface uncertain parameter identification method, which comprises the following steps: converting structure uncertain parameters p i into a function of standard random variables ξ i , expanding structure response y by using a chaos polynomial, and solving chaos polynomial coefficients a j ; through orthogonal test design of statistical characteristic values of the uncertain parameters p=[p1, p2, …], the chaos polynomial coefficients a j are expanded into a quadratic response surface form about the statistical characteristic values of the structure uncertain parameters pi, and a random response surface model of structure positive problem calculation is constructed. The application has the beneficial effects that the application proposes an uncertain parameter identification method of the structure random response surface by considering the structure uncertain parameters, constructing the structure random response surface by using the chaos polynomial and the quadratic response surface method, establishing a structure parameter identification inverse problem objective function, and identifying the statistical characteristic values of the structure uncertain parameters in steps, so that the calculation efficiency is higher than that of a traditional random response surface optimization method, and the application has very important engineering application value.

A client device is fabricated using a semiconductor fabrication process. One or more uncontrollable random physical processes in the semiconductor fabrication process can cause small differences between the client device and other client devices. When the client device is presented with a challenge from a server device, the client device generates a random response that depends on its physical properties. The server device stores this random response as a part of a virtual PUF circuitry storage device having other random responses from the other client devices. The server device uses the random response of the client device stored in the virtual PUF circuitry storage device for one or more encryption algorithms to encrypt information to be provided to the client device.

The application relates to the technical field of data privacy protection, and provides a sketch-based low-frequency item frequency estimation method under local differential privacy, which comprises the following steps: a user constructs a multi-layer sketch structure based on a local data set, a generalized random response mechanism is applied to disturb column coordinates of each layer of the sketch, so that a local differential privacy constraint is met; a server receives disturbed row coordinate and column coordinate pairs uploaded by all users, aggregates the disturbed row coordinate and column coordinate pairs into a global multi-layer sketch, constructs a global multi-layer sketch matrix, calibrates observation counts of each layer of the global multi-layer sketch matrix, estimates real frequencies of each layer of the global multi-layer sketch matrix, and based on the calibrated frequency estimation of each layer, a query item is calculated to obtain a global frequency estimation of the query item. By introducing the local differential privacy mechanism and optimizing the sketch structure, the application can significantly improve the frequency estimation precision of low-frequency data items while strictly protecting the individual privacy of the user.

The invention discloses a localized differential privacy protection method for ADS-B (Automatic Dependent Surveillance-Broadcast) positioning data. The method comprises the following steps: acquiring ADS-B original positioning data, and performing dimension reduction conversion and coding; performing staged random response operation on the coded positioning data; and performing frequency estimation and aggregation on the disturbance positioning data. The method has the advantages that a three-dimensional data coding scheme is adopted, positioning data including the latitude, longitude and altitude of an aircraft in ADS-B message data is coded, an efficient stage type grouping random response scheme is provided, the precision loss of the positioning data in the random response process is effectively reduced, and the positioning accuracy of the ADS-B message data is improved. By expanding a Kalman filter estimation method based on staged probability weighting, the accuracy of performing frequency estimation and frequency statistics on positioning data after random response is effectively improved. Compared with the existing LDP scheme, the method disclosed by the invention finds a good balance between privacy protection and data availability, and has better availability.

The invention relates to the technical field of electric vehicle and power grid interaction, and particularly discloses a V2G aggregator bidding decision-making method based on a master-slave random game. The relationship between the V2G aggregator and the electric vehicle user cluster is modeled as a master-slave random game, the aggregator is used as a master party to formulate an electricity price strategy, and the user cluster is used as a slave party to respond with a random charging and discharging behavior. A random response model of the slave party is constructed, the uncertainty of the random response model is quantified, and meanwhile a user participation degree evaluation index which is dynamically updated in real time is constructed; establishing a cooperative constraint distribution robust optimization CC-DRO decision model of the master party; and an improved COA-ESN hybrid intelligent algorithm is adopted to solve the CC-DRO model, a global optimal bidding strategy is output, the bidding strategy is executed, and market electric charge settlement is completed. Compared with the prior art, through fusion of a master-slave random game framework and multiple technologies, breakthrough of a V2G bidding strategy in robustness and economy is realized, and real-time performance of decision making is ensured through an efficient hybrid algorithm.

The application discloses a teacher consensus aggregation learning method based on a random response differential privacy technology, and comprises the following steps: inputting a query into a teacher model to obtain an output label corresponding to the teacher model; wherein the teacher model is multiple, and each teacher model is different from each other; performing random disturbance on the output label to obtain a disturbed label; determining an estimated label corresponding to the query according to the disturbed label; wherein the estimated label is at least two; training a student model based on a data set composed of the query and the estimated label and a data set generated by a generative adversarial network to obtain a trained student model. Since the sensitive data in the sensitive data set cannot be obtained through the query and the estimated label when the student model is migrated, the problem of improving the privacy in the migration learning is solved.

The embodiment of the application provides a power distribution network-multi-microgrid collaborative optimization method and related equipment, and belongs to the field of power system optimization scheduling. The method comprises the following steps: each operation subject constructs a chaotic polynomial proxy model of operation cost based on a random response surface method, and quantitatively obtains an expected independent operation cost of each subject considering uncertainty risk; the cost is used to reconstruct a negotiation breakdown point of a cooperative game; a Nash bargaining model is established based on a new benchmark, and is decomposed into power and price optimization sub-problems, distributed iterative solution is carried out by using an alternating direction multiplier method, and an optimal collaborative transaction strategy is obtained; and finally, the strategy is executed and dynamically updated under an intra-day rolling framework. The application realizes endogenous quantification and fair allocation of risk cost by deeply integrating the random response surface method and the game theory, overcomes the defects of the traditional "risk-game separation" paradigm, and effectively improves the overall economy, operation stability and benefit distribution fairness of the system.

The invention discloses a contract data sharing method based on an alliance chain and local differential privacy. The contract data sharing method comprises five entities and five task execution stages. The five entities are respectively an enterprise data provider, a Group 1 contract data recording chain, a Group 2 task management chain, an under-chain cross-group scheduler CGO and an under-chain computing node; the five task execution stages are respectively a contract data uplink stage, a task triggering and cross-group scheduling stage, an under-chain calculation node execution statistics stage, an under-chain credible aggregation stage, and an uplink and incentive distribution stage. According to the method, a multi-dimensional random response (MDRR) mechanism is designed, and the internal attribute relevance of data records is reserved while privacy is protected; a hybrid computing mechanism of alliance chain and differential privacy fusion is constructed, and on-chain scheduling and off-chain efficient computing are realized, so that the computing overhead is reduced; a data aggregation mechanism TUSO based on trust-utility collaborative optimization is provided, and the reliability and effectiveness of data sharing are improved by integrating trust score and data utility optimization.

The application discloses a kind of logic regression methods based on compression local differential privacy protection.In the method, user calculates gradient vector according to the model parameter released by server, and obtains input vector by encoding processing;By random response mechanism disturbance, output vector is obtained, and privacy protection is realized.Server aggregates and corrects output vector, obtains unbiased mean and can be used to update model parameter, and releases update parameter to the user participating in training in next round.After multiple iterations, logic regression model is trained.Finally, the unknown class label of user data can be classified and predicted by using the logic regression model.The application introduces compressed local differential privacy model, which improves the utility and estimation accuracy of data statistics while protecting user privacy, balances privacy protection and data availability, provides classification prediction for users while ensuring that attackers cannot reverse infer individual data in training data, and has high classification accuracy.