Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators | ScienceToStartup | ScienceToStartup

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References (22)

[1]

Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data

2023Florent Gu'epin, Matthieu Meeus et al.

[2]

Membership Inference Attacks against Synthetic Data through Overfitting Detection

2023B. V. Breugel, Hao Sun et al.

[3]

Synthcity: facilitating innovative use cases of synthetic data in different data modalities

Founder's Pitch

"A practical method to quantify membership disclosure risk in synthetic datasets using kernel density estimators."

Synthetic Data Privacy•Score: 8•View PDF ↗

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Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators

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References (22)

Founder's Pitch

"A practical method to quantify membership disclosure risk in synthetic datasets using kernel density estimators."

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Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators

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References (22)

Founder's Pitch

"A practical method to quantify membership disclosure risk in synthetic datasets using kernel density estimators."

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