2results about How to "Improve statistical accuracy" patented technology

This invention discloses a method and system for local differential privacy aggregate value data aggregation based on sketches and sampling, belonging to the field of privacy computing and big data analysis technology. The method includes: initializing and disclosing sketch structure parameters on the server side; constructing aggregate value data on the user side, randomly selecting counter indices and determining the hash collision set; randomizing the hash collision set elements on the user side according to the aggregation function type, calculating the hit rate and performing adaptive pruning; reporting triples on the user side; and accumulating and updating the sketch counter on the server side, calculating the aggregated statistics through median estimation. This invention replaces traditional multi-element perturbation with sketch sampling and lightweight reporting, without requiring additional privacy budget consumption, effectively solving the noise accumulation problem, significantly reducing computational and communication overhead, and achieving a balance between privacy protection and statistical utility.

The embodiment of the invention provides an inertia probability prediction method and device, and the method comprises the steps: constructing a comprehensive feature set containing basic operation features and real-time dynamic features, and enabling the comprehensive feature set to serve as the input of a prediction model; constructing a mixed risk objective function fusing an economic cost item, a decision risk cost item and a physical safety penalty item, wherein the mixed risk objective function is used for training a prediction model; and training a probability prediction model by adopting a risk-guided probability modeling method based on the mixed risk objective function so as to generate inertia probability distribution of the embedded decision risk and the security constraint. Through a risk-guided probability modeling method, complete probability distribution of the embedded decision preference and the security boundary is generated, so that the statistical accuracy of prediction is improved, and the prediction robustness in an extreme risk scene is ensured; and a more reliable inertia uncertainty description which better meets the decision demand is provided for fine scheduling and security defense of the power system in a new energy high-permeability environment.