Detecting Adversary using Windows Digital Artifacts. - Seng Pei Liew; Satoshi Ikeda (IEEE BigData, 2019)

POCO: A Voice Spoofing and Liveness Detection Corpus Based on Pop Noise. - Kosuke Akimoto; Seng Pei Liew; Sakiko Mishima; Ryo Mizushima; Kong Aik Lee (INTERSPEECH, 2020)

Secure IoT Device Architecture Using TrustZone. - Takayuki Sasaki; Koki Tomita; Yuto Hayaki; Seng Pei Liew; Norio Yamagaki (SECON Workshops, 2020)

FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries. - Seng Pei Liew; Tsubasa Takahashi 001 (CoRR, 2020)

Adversarial Robustness for Face Recognition: How to Introduce Ensemble Diversity among Feature Extractors? - Takuma Amada; Kazuya Kakizaki; Seng Pei Liew; Toshinori Araki; Joseph Keshet; Jun Furukawa 001 (SafeAI@AAAI, 2021)

Universal Adversarial Spoofing Attacks against Face Recognition. - Takuma Amada; Seng Pei Liew; Kazuya Kakizaki; Toshinori Araki (IJCB, 2021)

Advanced Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider. - Anna Stakia; Tommaso Dorigo; Giovanni Banelli; Daniela Bortoletto; Alessandro Casa; Pablo de Castro; Christophe Delaere; Julien Donini; Livio Finos; Michele Gallinaro; Andrea Giammanco; Alexander Held; et al. (CoRR, 2021)

PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning. - Seng Pei Liew; Tsubasa Takahashi 001; Michihiko Ueno (ICLR, 2022)

Network Shuffling: Privacy Amplification via Random Walks. - Seng Pei Liew; Tsubasa Takahashi 001; Shun Takagi; Fumiyuki Kato; Yang Cao 011; Masatoshi Yoshikawa (SIGMOD Conference, 2022)

Shuffled Check-in: Privacy Amplification towards Practical Distributed Learning. - Seng Pei Liew; Satoshi Hasegawa; Tsubasa Takahashi 001 (CoRR, 2022)

Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning. - Marin Matsumoto; Tsubasa Takahashi 001; Seng Pei Liew; Masato Oguchi (CoRR, 2022)

Shuffle Gaussian Mechanism for Differential Privacy. - Seng Pei Liew; Tsubasa Takahashi 001 (CoRR, 2022)

HDPView: Differentially Private Materialized View for Exploring High Dimensional Relational Data. - Fumiyuki Kato; Tsubasa Takahashi 001; Shun Takagi; Yang Cao 011; Seng Pei Liew; Masatoshi Yoshikawa (Proc. VLDB Endow., 2022)

Scaling Private Deep Learning with Low-rank and Sparse Gradients. - Ryuichi Ito; Seng Pei Liew; Tsubasa Takahashi 001; Yuya Sasaki 001; Makoto Onizuka (J. Inf. Process., 2023)

Estimating Visited Stores Through Positive-Unlabeled Learning. - Ryo Shirai; Ryo Imai; Seng Pei Liew; Daichi Amagata; Tsubasa Takahashi 001; Takahiro Hara (DASFAA (7), 2024)

Federated Graph Analytics with Differential Privacy. - Shang Liu 001; Yang Cao 011; Takao Murakami; Weiran Liu; Seng Pei Liew; Tsubasa Takahashi 001; Jinfei Liu; Masatoshi Yoshikawa (CoRR, 2024)

Measuring Local and Shuffled Privacy of Gradient Randomized Response. - Marin Matsumoto; Tsubasa Takahashi 001; Seng Pei Liew; Masato Oguchi (J. Inf. Process., 2024)

Scaling Laws for Upcycling Mixture-of-Experts Language Models. - Seng Pei Liew; Takuya Kato; Sho Takase (CoRR, 2025)

Accelerating Differentially Private Federated Learning via Adaptive Extrapolation. - Shokichi Takakura; Seng Pei Liew; Satoshi Hasegawa (CoRR, 2025)

FedDuA: Doubly Adaptive Federated Learning. - Shokichi Takakura; Seng Pei Liew; Satoshi Hasegawa (CoRR, 2025)