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March 3, 2026
Roughness-informed differential privacy
MP
Mohammad Partohaghighi
MR
Marcia F. Roummel
BW
Bruce J. West
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Key Points
Roughness-informed differential privacy significantly reduces algorithmic bias while maintaining data utility.
The use of statistical noise enhances the privacy of sensitive data without sacrificing accuracy or access.
Analysis explores how roughness metrics shape algorithm effectiveness in protecting privacy across diverse datasets.
Findings clarify the potential for advanced privacy mechanisms to offer robust data protection in real-world applications.
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Partohaghighi et al. (Thu,) studied this question.
synapsesocial.com/papers/69a76743badf0bb9e87e037e
https://doi.org/https://doi.org/10.1016/j.eswa.2026.131501
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Roughness-informed differential privacy | Synapse