핵심 개념
The author explores the convergence rate of partitioning classification under relaxed conditions for observable and privatised data, introducing novel assumptions to calculate the error probability's exact convergence rate.
초록
In this paper, the authors delve into partitioning classification methods for observable and privatised data. They introduce new assumptions to determine the convergence rate of error probabilities accurately. The study focuses on binary and multi-label classification cases, emphasizing observable and anonymised data scenarios. By relaxing strong density assumptions, they derive optimal rates based on intrinsic dimensions. The research challenges existing optimal error bounds by considering Laplace-type randomisation for privacy constraints in classifying sensitive data.
The content covers fundamental statistical problems related to classification in various fields such as health care, industry, commerce, and finance. It discusses differential privacy frameworks to ensure information security while processing sensitive data. The study provides insights into the convergence rates of classification errors under different conditions for both observable and privatised datasets.
통계
Previous results worked with a strong density assumption restricting flexibility.
Privacy mechanisms involve Laplace distributed noises for anonymisation.
Convergence rates depend on intrinsic dimensionality parameters.
Optimal rates are achieved without stringent density assumptions.
Tight upper bounds are derived for error probability convergence rates.
Multi-label extensions are considered with generalized margin conditions.
Local differential privacy mechanisms enhance data security during processing.
Novel characterizations improve estimation errors based on margin-dense relations.