Publications

Select-Then-Compute: Encrypted Label Selection and Analytics over Distributed Datasets using FHE

Published in Network and Distributed System Security (NDSS)., 2026

This work introduces Select-Then-Compute, the first encrypted label selection and analytics protocol that extends PSI to securely retrieve labels for intersected identifiers and evaluate downstream real-valued functions (e.g., ML inference) over those labels using CKKS FHE.

Recommended citation: N. Koirala, S. Paik, S. Martin, H. Berens, T. Januszewicz, J. Takeshita, J.H. Seo, & T. Jung. (2026, February). Select-Then-Compute: Encrypted Label Selection and Analytics over Distributed Datasets using FHE. In 33rd Annual Network and Distributed System Security (NDSS 2026). https://eprint.iacr.org/2025/2023

HyDia: FHE-based Facial Matching with Hybrid Approximations and Diagonalization

Published in Privacy Enhancing Technologies Symposium (PETS) 2025, 2025

This paper presents HyDia, a facial matching framework built on fully homomorphic encryption that leverages hybrid approximations and diagonalization to achieve efficient and privacy-preserving biometric comparisons.

Recommended citation: Sam Martin, Nirajan Koirala, Helena Berens, Thomas Rozgonyi, Micah Brody, Taeho Jung. (2025). 'HyDia: FHE-based Facial Matching with Hybrid Approximations and Diagonalization.' Proceedings on Privacy Enhancing Technologies, 2025(3). https://petsymposium.org/popets/2025/popets-2025-0146.php

HEProfiler: An In-Depth Profiler of Approximate Homomorphic Encryption Libraries

Published in Journal of Cryptographic Engineering, 2025

This paper presents HEProfiler, a comprehensive profiling tool for approximate homomorphic encryption libraries, enabling detailed performance analysis and comparison across different implementations.

Recommended citation: Nirajan Koirala, Jonathan Takeshita, Colin McKechney, Taeho Jung. (2025). 'HEProfiler: An In-Depth Profiler of Approximate Homomorphic Encryption Libraries.' Journal of Cryptographic Engineering, 15(2), 14. https://link.springer.com/article/10.1007/s13389-025-00377-5

Summation-based Private Segmented Membership Test from Threshold-Fully Homomorphic Encryption

Published in ‘Privacy Enhancing Technologies 2024’, 2024

‘This paper introduces a novel Private Segmented Membership Test (PSMT) protocol utilizing threshold-based homomorphic encryption, enabling secure queries across multiple data holders without compromising privacy.’

Recommended citation: ‘Nirajan Koirala, Jonathan Takeshita, Jeremy Stevens, Taeho Jung. (2024). "Summation-based Private Segmented Membership Test from Threshold-Fully Homomorphic Encryption." Privacy Enhancing Technologies 2024.’ ‘https://eprint.iacr.org/2024/753.pdf’