This blog post is based on the paper “Efficient Pseudorandom Correlation Generators for Any Finite Field” presented by Yizhou Yao in Madrid at Eurocrypt 2025. The paper by Zhe Li, […]
This blog post summarizes our contribution and main ideas of the paper “Fast Evaluation of S-boxes with Garbled Circuits” published in IEEE Transactions on Information Forensics and Security (paper link, […]
This blog post summarizes the talk by Lucien K. L. Ng about the paper “Garbled Circuit Lookup Tables with Logarithmic Number of Ciphertexts” at Eurocrypt 2024. The paper is co-authored […]
This blogpost summarizes our paper “Let’s Go Eevee! A Friendly and Suitable Family of AEAD Modes for IoT-to-Cloud Secure Computation” by authors Amit Singh Bhati, Erik Pohle, Aysajan Abidin, Elena […]
This blog post summarizes this paper which is joint work with Aysajan Abidin and Bart Preneel and was presented at ESORICS 2023. Code for the paper can be found here. […]
Christian Rechberger (TU Graz, Austria) gave a talk titled “On the history of MPCFHEZK-friendly symmetric crypto” at STAP’23. In his talk, he revisited both the history and creation of arithmetic […]
Pseudorandom correlation generators (PCGs) are a promising line of research to reduce the communication complexity of the pre-processing phase of many MPC protocols. In her invited talk at TPMPC 2022, Lisa Kohl gave an introduction to PCGs before presenting novel work on a PCG construction based on Expand-Acumulate codes in the learning parity with noise (LPN) setting, which is due to appear in Crypto 2022. The constructions shown in the talk focus on a two-party secure computation.
Garbled Circuits are one fundamental technique for secure two-party computation. The presentation by David Heath at Eurocrypt 2022 introduces an improvement of previous work for garbling functions with branches. While in the classic garbled circuit setting branching on secret values cannot be supported as efficiently as branches in cleartext computation, the work of Stacked Garbling [Heath21] and this follow-up work, supports function evaluation with a large number of branches more efficiently. They also sketch an application for garbled private information retrieval (PIR).
