OpenAlex | Covariance’s Loss is Privacy’s Gain: Computationally Efficient, Private and Accurate Synthetic Data

Covariance’s Loss is Privacy’s Gain: Computationally Efficient, Private and Accurate Synthetic Data

Work

Year: 2022

Type: article

Abstract: The protection of private information is of vital importance in data-driven research, business and government. The conflict between privacy and utility has triggered intensive research in the compute... more

Source: Foundations of Computational Mathematics

Authors March Boedihardjo, Thomas Strohmer, Roman Vershynin

Institutions ETH Zurich, University of California, Davis, University of California, Irvine

Cites: 39

Cited by: 13

Related to: 10

FWCI: 1.877

Citation percentile (by year/subfield): 100

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Sustainable Development Goal Peace, justice, and strong institutions

Open Access status: hybrid

APC paid (est): $2,990