Detecting and mitigating poison attacks using data provenance
US11689566B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 10, 2018 |
| Grant date | Jun 27, 2023 |
| Priority date | — |
| Expiry date | Mar 9, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2463/145
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Computer-implemented methods, program products, and systems for provenance-based defense against poison attacks are disclosed. In one approach, a method includes: receiving observations and corresponding provenance data from data sources; determining whether the observations are poisoned based on the corresponding provenance data; and removing the poisoned observation(s) from a final training dataset used to train a final prediction model. Another implementation involves provenance-based defense against poison attacks in a fully untrusted data environment. Untrusted data points are grouped according to provenance signature, and the groups are used to train learning algorithms and generate complete and filtered prediction models. The results of applying the prediction models to an evaluation dataset are compared, and poisoned data points identified where the performance of the filtered prediction model exceeds the performance of the complete prediction model. Poisoned data points are removed from the set to generate a final prediction model.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.