Associate Professor Anamaria Costache from the Norwegian University of Science and Technology (NTNU) will present a seminar on Fully Homomorphic Encryption (FHE). The talk will cover recent advancements in FHE, its mathematical foundations, and implementation results. It will also address remaining challenges in the field. Why it matters: FHE's growing importance is driven by Machine Learning as a Service and the increasing value of secure computation, though the seminar itself has no direct connection to the Middle East.
Technology Innovation Institute (TII) in Abu Dhabi has launched the UAE’s first secure cloud technologies programme via its Cryptography Research Center (CRC). The program will focus on advancing Privacy Enhancing Technologies (PETs) like fully homomorphic encryption (FHE) and secure multi-party computation (MPC). TII researchers are also developing hardware accelerators to improve the efficiency of FHE. Why it matters: The initiative addresses growing security and privacy challenges in cloud computing, positioning the UAE as a leader in advanced cryptographic solutions for data protection.
CINVESTAV-IPN's Computer Science Department hosted a seminar by Prof. Francisco Rodriguez-Henriquez on isogeny-based key exchange protocols. The talk reviewed Supersingular Isogeny-based Diffie-Hellman (SIDH) and Commutative Supersingular Isogeny-based Diffie-Hellman (CSIDH). Isogeny-based protocols offer short key sizes but have higher latency compared to other post-quantum cryptosystems. Why it matters: This seminar contributes to the exploration of post-quantum cryptography, an important area for ensuring data security against future quantum computing threats.
The National Institute of Standards and Technology (NIST) has been evaluating Post-Quantum Cryptography proposals since 2017. Lattice-based schemes have emerged as efficient candidates for Key Encapsulation Mechanisms (KEM) and Digital Signatures. This talk will cover the core operations within lattice-based schemes and efficient implementation strategies. Why it matters: As quantum computing advances, exploring and standardizing post-quantum cryptography is crucial for maintaining secure communication and data protection in the future.
A cryptanalysis team at the UAE's Cryptography Research Center (CRC) has set new records in computation by decrypting a McEliece ciphertext without the secret key at INRIA’s McEliece decoding challenge, taking first and second place. The record computation took about 31.4 days on a cluster using 256 CPU-cores. The team also achieved top ranks in decoding quasi-cyclic codes and ternary codes, used in post-quantum cryptography. Why it matters: This achievement demonstrates the UAE's growing capabilities in advanced cryptography research and its contributions to the global effort to develop quantum-resistant algorithms.
Prof. Daniel Panario gave a seminar on irreducible polynomials over finite fields and their applications in cryptography. The seminar covered how finite fields are used as basic components in many cryptographic applications. It surveyed families of irreducible polynomials and commented on their properties. Why it matters: The talk highlights the mathematical foundations and ongoing research relevant to cryptographic implementations in the region.
Professor Mike Scott will present a seminar at the Technology Innovation Institute's Cryptography Research Centre in the UAE. The seminar will focus on the challenges of keeping secrets safe from attackers in the context of cryptography. It will review proposed solutions, discuss use cases, and present a promising new approach. Why it matters: This seminar indicates TII's ongoing research and development efforts in advanced cryptography, a crucial area for secure digital infrastructure in the UAE and beyond.
MBZUAI Assistant Professor Samuel Horváth is researching federated learning to address the tension between data privacy and the predictive power of machine learning models. Federated learning trains models on decentralized data, keeping sensitive information on devices. Horváth's research focuses on designing algorithms that can efficiently train on distributed data while respecting user privacy. Why it matters: This work is crucial for advancing AI in sensitive domains like healthcare, where privacy regulations limit centralized data collection.