The field of communication and privacy-preserving protocols is witnessing significant advancements, driven by the need for efficient and secure data exchange. Researchers are exploring innovative approaches to reduce communication complexity, enhance privacy, and develop robust protocols. Notably, the development of subquadratic two-party communication protocols and public-key encryption schemes from the MinRank problem are pushing the boundaries of secure communication. Furthermore, the design of adaptive receive scaling factors in over-the-air federated learning and the analysis of trade-offs in estimating the number of Byzantine clients are contributing to the advancement of privacy-preserving protocols. The creation of novel schemes for multi-agent distributed optimization with feasible set privacy and the enhancement of TreePIR for single-server settings are also noteworthy.

Particularly noteworthy papers include: A Subquadratic Two-Party Communication Protocol for Minimum Cost Flow, which presents a randomized algorithm for linear programs with two-sided constraints that improves upon prior work. Public-Key Encryption from the MinRank Problem, which constructs a public-key encryption scheme from the hardness of the MinRank problem. Privacy Enhancement in Over-the-Air Federated Learning via Adaptive Receive Scaling, which develops an online algorithm to balance the trade-off between training convergence and privacy. Enhancing TreePIR for a Single-Server Setting via Resampling, which proposes an adaptation of TreePIR to the single-server setting with improved communication and storage efficiency.