The vulnerability of multiplicative noise protection to correlation-attacks on continuous microdata

DOI10.1007/s13571-019-00191-0zbMath1465.62027OpenAlexW2926880294WikidataQ128152209 ScholiaQ128152209MaRDI QIDQ2023796

Rathindra Sarathy, Yue Ma, Yan-Xia Lin

Publication date: 3 May 2021

Published in: Sankhyā. Series B (Search for Journal in Brave)

Full work available at URL: https://ro.uow.edu.au/eispapers1/2907

zbMATH Keywords

data confidentialitydisclosure riskattacking strategycontinuous microdatanoise multiplication masking

Mathematics Subject Classification ID

Foundations and philosophical topics in statistics (62A01) Admissibility in statistical decision theory (62C15)

Related Items (1)

INVESTIGATING ATTRIBUTE RISKS AND CONSTRUCTING LINKAGE ERROR MODELS FOR PROBABILISTICALLY-LINKED DATA

Uses Software

• MaskDensity14

Cites Work

• Masking methods that preserve positivity constraints in microdata
• Privacy protection and quantile estimation from noise multiplied data
• Multiplicative Errors-in-Variables Models with Applications to Recent Data Released by the U.S. Department of Energy
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