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Privacy-Preserving High-dimensional Data Collection with Federated Generative Autoencoder
2021
Proceedings on Privacy Enhancing Technologies
Business intelligence and AI services often involve the collection of copious amounts of multidimensional personal data. Since these data usually contain sensitive information of individuals, the direct collection can lead to privacy violations. Local differential privacy (LDP) is currently considered a state-ofthe-art solution for privacy-preserving data collection. However, existing LDP algorithms are not applicable to high-dimensional data; not only because of the increase in computation and
doi:10.2478/popets-2022-0024
fatcat:z55qkdtnc5dmxp3hatsmmkolwe