Information Security seminars

Oren Renard studies security of quantum position verification based on f-routing. The work derives resource lower bounds for uniformly generated attackers by connecting successful attacks to the computational complexity of the routed function. Separate results cover arbitrary quantum channels, Pauli-sparse unitaries and Clifford+T circuits. Time and space hierarchies yield explicit functions secure against polynomial or quasipolynomial numbers of attacker qubits under these computational restrictions, extending earlier bounds while increasing the complexity of honest evaluation.

Sanjam Garg explores encryption whose decryption condition is solving a puzzle or proving a mathematical statement, rather than simply possessing a conventional cryptographic key. The lecture traces the development of this idea and describes recent advances that are making such schemes more practical for real applications.

Brendan McMahan examines how AI systems can learn from high-quality information that is distributed and privacy-sensitive. He introduces principles for precise discussions of privacy and AI, then connects them to the theory and practical methods needed in real applications, including work on decentralised and privacy-preserving learning.

Katrina Ligett explores why learning theory can make pessimistic predictions even when practical systems succeed. Instead of analysing only worst-case algorithms and data, she proposes reformulating questions around properties that explain algorithmic success and instance-specific performance. Examples in generalisation and privacy illustrate how these perspectives could narrow the gap between theory and empirical practice and create new mathematical questions.

Ryann Cartor introduces multivariate public-key cryptography, whose fast and compact post-quantum schemes use polynomial systems over finite fields. The tutorial surveys past and current multivariate cryptosystems before examining MinRank as a central cryptanalytic problem. MinRank attacks have broken prominent schemes in both multivariate and code-based cryptography. The material assumes basic algebra and connects these foundations to current research directions.

José Ramón Pareja Monturiol presents a method that converts black-box functions into tensor-train representations using sketching, cross interpolation and a small sample of the relevant domain. For neural networks, this enables privacy-oriented obfuscation and interpretable estimates of topological phases. The method also supplies an efficient initialisation for tensor-train optimisation and improves the memory–computation trade-off in model compression.