On the Inherent Privacy Amplification of Missing Data

arXiv:2602.01928 · stat.ML, cs.LG · Submitted 2026-02-02 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-02-02

Updated: 2026-09-17

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Differential Privacy — 23× in this paper · explained in 4 episodes
• Differentially Private — 10× in this paper · explained in 3 episodes
• Missing Completely At Random — 3× in this paper
• Accuracy — 2× in this paper · explained in 1 episode
• Missing At Random — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Algorithmic Primitives — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Missing Not At Random — 1× in this paper
• Representation Learning — 1× in this paper · explained in 2 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Imputation under Differential Privacy
• Differentially Private Data Generation with Missing Data

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