We specify OCH, the first authenticated encryption with associated data scheme built to provide 128-bit multi-user AE security, 128-bit context commitment security, and 256-bit nonces with optional nonce privacy. It therefore addresses pressing limitations of currently widely-deployed schemes. We construct and formally analyze the security of OCH in a modular fashion, with transforms that are of broader applicability. On Intel Raptor Lake CPUs, OCH using the Areion permutation family has a peak encryption speed of 0.62 cycles per byte (cpb), not far off from AES128-GCM (0.38cpb) and outperforming both ChaCha20/Poly1305 (1.63cpb) and TurboSHAKE128-Wrap (3.52cpb).

The recent Digital Markets Act (DMA), a regulation passed by the European Union in 2022, requires messaging applications with large user bases to support interoperable end-to-end encrypted (E2EE) communication. This raises numerous questions about how to adapt cryptographic protocols to this setting in a way that preserves security and privacy. This question is not only limited to the main messaging protocols, but also extends to protocols for abuse mitigation such as the symmetric message franking protocol first proposed by Facebook. The latter uses symmetric cryptography to enable reporting abusive E2EE messages in a way that allows the platform to cryptographically verify the report's veracity.

Key Transparency (KT) systems enable service providers of end-to-end encrypted communication (E2EE) platforms to maintain a Verifiable Key Directory (VKD) that maps each user's identifier, such as a username or email address, to their identity public key(s). Users periodically monitor the directory to ensure their own identifier maps to the correct keys, thus detecting any attempt to register a fake key on their behalf to Meddler-in-the-Middle (MitM) their communications.

Many privacy-preserving protocols employ a primitive that allows a sender to "flag" a message to a recipient's public key, such that only the recipient (who possesses the corresponding secret key) can detect that the message is intended for their use. Examples of such protocols include anonymous messaging, privacy-preserving payments, and anonymous tracing. A limitation of the existing techniques is that recipients cannot easily outsource the detection of messages to a remote server, without revealing to the server the exact set of matching messages. In this work we propose a new class of cryptographic primitives called \em fuzzy message detection schemes. These schemes allow a recipient to derive a specialized message detection key that can identify correct messages, while also incorrectly identifying non-matching messages with a specific and chosen false positive rate p. This allows recipients to outsource detection work to an untrustworthy server, without revealing precisely which messages belong to the receiver. We show how to construct these schemes under a variety of assumptions; describe several applications of the new technique; and show that our schemes are efficient enough to use in real applications.

Motivation: Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research. Results: The tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism. Availability and implementation: The visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

The MD transform that underlies the MD and SHA families iterates a compression function h to get a hash function H. The question we ask is, what property X of h guarantees collision resistance (CR) of H? The classical answer is that X itself be CR. We show that weaker conditions X, in particular forms of what we call constrained-CR, suffice. This reduces demands on compression functions, to the benefit of security, and also, forensically, explains why collision-finding attacks on compression functions have not, historically, lead to immediate breaks of the corresponding hash functions. We obtain our results via a definitional framework called RS security, and a parameterized treatment of MD, that also serve to unify prior work and variants of the transform.