Efficient unsupervised anomaly detection on homomorphically encrypted data
US11271958B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 20, 2019 |
| Grant date | Mar 8, 2022 |
| Priority date | — |
| Expiry date | Nov 11, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S40/20
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Aspects of the present disclosure describe techniques for detecting anomalous data in an encrypted data set. An example method generally includes receiving a data set of encrypted data points. A tree data structure having a number of levels is generated for the data set. Each level of the tree data structure generally corresponds to a feature of the encrypted plurality of features, and each node in the tree data structure at a given level represents a probability distribution of a likelihood that each data point is less than or greater than a split value determined for a given feature. An encrypted data point is received for analysis, and anomaly score is calculated based on a probability identified for each of the plurality of encrypted features. Based on determining that the calculated anomaly score exceeds a threshold value, the encrypted data point is identified as potentially anomalous.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.