Oblivious Transfer (OT) is a fundamental cryptographic protocol with applications in secure Multi-Party Computation, Federated Learning, and Private Set Intersection. With the advent of quantum computing, it is crucial to develop unconditionally secure core primitives like OT to ensure their continued security in the post-quantum era. Despite over four decades since OT's introduction, the literature has predominantly relied on computational assumptions, except in cases using unconventional methods like noisy channels or a fully trusted party. Introducing "Supersonic OT", a highly efficient and unconditionally secure OT scheme that avoids public-key-based primitives, we offer an alternative to traditional approaches. Supersonic OT enables a receiver to obtain a response of size O(1). Its simple (yet non-trivial) design facilitates easy security analysis and implementation. The protocol employs a basic secret-sharing scheme, controlled swaps, the one-time pad, and a third-party helper who may be corrupted by a semi-honest adversary. Our implementation and runtime analysis indicate that a single instance of Supersonic OT completes in 0.35 milliseconds, making it up to 2000 times faster than the state-of-the-art base OT.

The first two generations of cellular--1G/2G--enabled ubiquitous voice connectivity. Even generations introduced services for business customers, and odd generations democratized them for consumers. One main avenue for achieving this is cost reduction.6 Another avenue is radio access with joint communications and sensing.7 New services are envisioned, such as low-altitude air traffic control, detecting, for example, bird migration and adapting drone services accordingly. Every opportunity of improving sensing is an opportunity for spying.

Data collaboration is the process of combining datasets together to generate new value from data-driven insights. The datasets being combined can come from different organizations, or they can come from data silos internal to an organization. A number of use cases are possible through data collaboration: fraud detection, advances in healthcare research, real-world data, cross-selling, churn analysis, etc. However, there are significant blockers in realizing the potential benefits of data collaboration. Some of these blockers are so severe that they can stymie potentially valuable collaborations. The blockers originate from a host of areas -- fear of loss of IP (intellectual property), privacy regulations, data residency restrictions, and reputational risk (just to name a few).