Munich Workshop on Coding and Cryptography 2024

Contributed Posters

  • Houman Asgari (TUM): Age of Information for Frame Asynchrounous Slotted ALOHA
  • Anna Baumeister (TUM): Erasure Decoding of LRPC codes via Row Support
  • Sebastian Bitzer (TUM): The Restricted Syndrome Decoding Problem
  • Marvin Geiselhart (University of Stuttgart): Row-Merged Polar Codes: Analysis and Design
  • Christoph Hofmeister (TUM): Achieving DNA Labeling Capacity with Minimum Labels through Extremal de Bruijn Subgraphs
  • Alex Jäger (TUM): Successive Interference Cancellation for Optical Fiber Using Discrete Constellations
  • Tayyebeh Jahani-Nezhad (TU Berlin): ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment
  • Diego Lentner (TUM): Low Latency Coding for Datacenter Interconnects
  • Hedongliang Liu (TUM): Support-Constrained Codes for Multi-Source Network Coding
  • Georg Maringer (TUM): Ciphertext size bounds for sharing a key using Kyber
  • Luis Maßny (TUM): Attacks and Defenses for Over-the-Air SGD
  • Vivian Papadopoulou (TUM): Sequence Reconstruction over Exact-t Adversarial Channel Repetitions: An Average Case Analysis
  • Daniel Plabst (TUM): Neural network equalization for bandlimited nonlinear channels
  • Anmoal Porwal (TUM): Code-Based Cryptography based on Supercode Decoding
  • Stefan Ritterhoff (TUM): FuLeeca and FuLeakage
  • Constantin Runge (TUM): Improved list-decoding for polar coded shaping
  • Vladimir Sidorenko (TUM) joint work with Victor Zyablov and Vladimir Potapov: A network with data protection from both channel errors and unauthorized access using one code
  • Thomas Wiegart (TUM): Probabilistic Shaping for Asymmetric Channels and LDPC Codes
  • Saar Tarnopolsky (Technion): Coding-Based Hybrid Post-Quantum Cryptosystem for Non-Uniform Information
  • Marvin Xhemrishi (TUM): FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
  • Yue Xia (TUM): Byzantine-resilient and Information-Theoretically Private Federated Learning
  • Lorenzo Zaniboni (TUM): Beam Alignment with an Intelligent Reflecting Surface for Integrated Sensing and Communciation