Generating relaxed synthetic data using adaptive projection
US11487765B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 28, 2021 |
| Grant date | Nov 1, 2022 |
| Priority date | — |
| Expiry date | Jun 28, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/2462
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An algorithm releases answers to very large numbers of statistical queries, e.g., k-way marginals, subject to differential privacy. The algorithm answers queries on a private dataset using simple perturbation, and then attempts to find a synthetic dataset that most closely matches the noisy answers. The algorithm uses a continuous relaxation of the synthetic dataset domain which makes the projection loss differentiable, and allows the use of efficient machine learning optimization techniques and tooling. Rather than answering all queries up front, the algorithm makes judicious use of a privacy budget by iteratively and adaptively finding queries for which relaxed synthetic data has high error, and then repeating the projection. The algorithm is effective across a range of parameters and datasets, especially when a privacy budget is small or a query class is large.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.