ConsentDiff enables longitudinal tracking of privacy policy churn and consent UI patterns, finding ongoing changes, shifts away from high-friction banners, and higher policy-UI alignment when rejection options are visible.
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2 Pith papers cite this work. Polarity classification is still indexing.
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PABIDOT is a new perturbation algorithm for privacy-preserving big data classification that claims superior execution speed, scalability, attack resistance, and accuracy versus two related methods across nine datasets and five classifiers.
citing papers explorer
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ConsentDiff at Scale: Longitudinal Audits of Web Privacy Policy Changes and UI Frictions
ConsentDiff enables longitudinal tracking of privacy policy churn and consent UI patterns, finding ongoing changes, shifts away from high-friction banners, and higher policy-UI alignment when rejection options are visible.
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Efficient privacy preservation of big data for accurate data mining
PABIDOT is a new perturbation algorithm for privacy-preserving big data classification that claims superior execution speed, scalability, attack resistance, and accuracy versus two related methods across nine datasets and five classifiers.