Norm-Based Outlier Filtering and Consensus Aggregation for Robust Federated Learning
IEEE AccessJournal (DOAJ)
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Jun-Woo Lee
중앙대학교 소프트웨어학부
Private Aritifical Intelligence and Cryptography Lab
We are looking for graduate students interested in researching private AI and cryptography, including the latest cryptographic techniques such as homomorphic encryption, post-quantum cryptography, and multi-party computation.
Privacy-preserving machine learning (PPML) combines cryptographic techniques like Homomorphic Encryption (HE), Multi-party Computation (MPC), and Differential Privacy (DP) with AI systems to maintain data privacy while performing AI models.
Analyzes security issues in federated learning, researches passive and active attack scenarios, and develops effective defense techniques against them.
Research on advanced encryption schemes for secure computations and fine-grained data access while maintaining security. Representative fields include Homomorphic Encryption (HE), Multi-party Computation (MPC), and Functional Encryption (FE).
Analyzes the comprehensive impact of quantum computers on cryptographic algorithms and the replacement of existing cryptographic algorithms by quantum computers.
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