5 papers
TraHist: Fast and Accurate Cardinality Estimation for Trajectory Data (Extended Version)
Yujeong Jo, Incheol Baek, Changbeom Shim +1
Cardinality estimation is fundamental for query optimization in database systems. Histogram-based estimators are widely used for their simplicity and efficiency but rely on point-c…
ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge
Incheol Baek, Hyungbin Kim, Yon Dohn Chung
Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data. A fundamental challenge, however, is that existing LDP mechanisms require a prede…
Federated Learning with Feedback Alignment
Incheol Baek, Hyungbin Kim, Minseo Kim +1
Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy, yet it struggles with data heterogeneity, where clients' data are not…
N-output Mechanism: Estimating Statistical Information from Numerical Data under Local Differential Privacy
Incheol Baek, Yon Dohn Chung
Local Differential Privacy (LDP) addresses significant privacy concerns in sensitive data collection. In this work, we focus on numerical data collection under LDP, targeting a sig…
Decoupled Contrastive Learning for Federated Learning
Hyungbin Kim, Incheol Baek, Yon Dohn Chung
Federated learning is a distributed machine learning paradigm that allows multiple participants to train a shared model by exchanging model updates instead of their raw data. Howev…