The field of secure computing and cryptography is rapidly evolving, with a focus on developing innovative solutions to protect sensitive data and ensure the integrity of computational processes. Recent research has explored the development of efficient and secure architectures for post-quantum cryptography, homomorphic encryption, and privacy-preserving machine learning. Notably, the use of trusted execution environments and hardware-algorithm co-design has shown promise in enhancing the security and efficiency of multi-party computation protocols. Furthermore, advancements in elliptic curve cryptography and fully homomorphic encryption have improved the security and performance of various cryptographic primitives. Overall, the field is moving towards the development of more efficient, scalable, and secure solutions for a wide range of applications, including cloud computing, IoT, and blockchain. Noteworthy papers include: A Constant-Time Hardware Architecture for the CSIDH Key-Exchange Protocol, which presents a comprehensive hardware study of CSIDH, and HEIR: A Universal Compiler for Homomorphic Encryption, which introduces a unified approach to building homomorphic encryption compilers. Additionally, Leuvenshtein: Efficient FHE-based Edit Distance Computation with Single Bootstrap per Cell presents a novel approach to calculating edit distance within the framework of fully homomorphic encryption.
Advancements in Secure Computing and Cryptography
Sources
A Constant-Time Hardware Architecture for the CSIDH Key-Exchange Protocol
HEIR: A Universal Compiler for Homomorphic Encryption
Activate Me!: Designing Efficient Activation Functions for Privacy-Preserving Machine Learning with Fully Homomorphic Encryption
Certified Compilation based on G\"odel Numbers
PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption
Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
CryptPEFT: Efficient and Private Neural Network Inference via Parameter-Efficient Fine-Tuning
Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds
AuthenTree: A Scalable MPC-Based Distributed Trust Architecture for Chiplet-based Heterogeneous Systems
Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design
Optimizing Scalar Selection in Elliptic Curve Cryptography Using Differential Evolution for Enhanced Security
Leuvenshtein: Efficient FHE-based Edit Distance Computation with Single Bootstrap per Cell