5 results about "Deterministic algorithm" patented technology

Embodiments include systems and methods for searching, tracing, and tracking Monero transaction chains. An exemplary method includes receiving, at a user interface using a plurality of Monero transaction menus, Monero transaction identification data for searching, tracing, and tracking Monero transaction chains. The method also includes determining, at the server computing device, an input of the Monero transaction identification data and an output of the Monero transaction identification data. The method also includes analyzing, at the server computing device, the input of the Monero transaction identification data determining a ring signature of the input of the Monero transaction identification data, the ring signature may include a plurality of decoys and a spendable and valid input. The method also includes generating, with a processor, a user interface displaying the plurality of decoys and the spendable and valid input.

The application discloses a bus passenger alighting station inference method based on a travel chain and an integrated learning, and comprises the following steps: collecting bus multi-source data; performing data cleaning and preprocessing on the multi-source data; fusing the multi-source data and judging a passenger boarding station according to a station arrival time window; performing preliminary inference on the alighting station based on a deterministic algorithm; establishing a deterministic algorithm to infer the passenger alighting station according to a travel chain assumption and passenger travel history; performing supplementary inference on the alighting station based on the integrated learning; introducing built environment and bus line external information for a journey that cannot be inferred by the deterministic algorithm; and establishing a multi-classification model based on an integrated learning two-layer stacking framework to further infer the passenger alighting station. The application realizes non-aggregated, station-level single-ticket bus passenger alighting station inference based on bus multi-source big data, can obtain accurate bus passenger flow space-time distribution characteristics, and provides a reference for bus operation organization optimization.

The application discloses a layered decoupling deterministic idempotent identification generation method and system. The method physically separates the session access stage and the service identification generation stage: the session access stage generates a session access certificate based on non-service attributes to prevent replay attacks; the service identification generation stage only constructs a service unique key based on service parameters and generates an idempotent handle through a deterministic algorithm; a minute boundary double window atomic query and TTL guarantee threshold checking mechanism are adopted to provide a clock drift tolerance of ±30 seconds; and offline verification capability is realized through reverse verification of the server. The application eliminates the long-term bidirectional mapping index of the service key to the identification, and the storage overhead can be reduced by more than 70%, the generation delay can be optimized to less than 1ms, and 100,000 + high-concurrency scenarios per second are supported; 100% cross-node consistency, zero-downtime configuration hot update, financial-grade security salt value rotation and cross-system offline verification capability are provided, and the application can be widely applied to distributed payment, e-commerce transaction and other scenarios.

This disclosure relates to a method and system for sanitization of sensitive data. The method includes analyzing sensitive data present within a page of an application based on a deterministic algorithm. Further, the method includes classifying the sensitive data into a high risk sensitive data and a low risk sensitive data based on a ML classification algorithm. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a destructive sanitization on each of the high risk sensitive data. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a non-destructive sanitization on each of the high risk sensitive data.

A minimal cluster of G+1 disks with G same-size cells has protection group members distributed in accordance with a deterministic algorithm such that the cell at disk X, cell Y is assigned to group (X+Y−1) modulo M, where M is group size (G) plus 1 and one of the groups is reserved as spare capacity for recovering from drive failure. The cluster can be granularly expanded and eventually split into a non-flex cluster and a flex cluster that begins with the member distribution pattern of the deterministic algorithm. Relocations of members during expansion and split are achieved in part by submatrix transposition and reordering of disks.