Secure messaging groups in applications such as Signal, Telegram, and Whatsapp are nowadays used for rapid and widespread dissemination of information to large groups of people. This is common even in sensitive contexts, involving the organisation of protests, activist groups, and internal company dialogues, for instance. Manual administration of who has access to such groups quickly becomes infeasible, in the presence of even hundreds of members. We construct a practical, privacy-preserving reputation protocol, that automates the approval of new group members based on their reputation amongst the existing membership. We prove security against malicious adversaries in a single-server model, with no further trust assumptions required, while supporting arbitrary reputation calculations even when almost all group members are offline (as is likely). We demonstrate the practicality of the approach experimentally: for groups of size 50 (resp. 500), admitting a user that received 40 (resp. 80) scores requires 1312.2~KiB (resp. 13086.3~KiB) of communication, and 3.4~s (resp. 42.3~s) of single-threaded computation. While our protocol design matches existing secure messaging applications, we believe it can have value in distributed reputation computation beyond this problem setting.

We propose Pool: a conceptually simple post-quantum (PQ) oblivious pseudorandom function (OPRF) protocol, that is round-optimal (with input-independent preprocessing), practically efficient, and has security based on the well-understood hardness of the learning with rounding (LWR) problem. Specifically, our design permits oblivious computation of the LWR-based pseudorandom function Fsk(x) = ⌉ H(x)⊤ ⋅ sk ⌋q,p, for random oracle H: {0,1} * → ℤ qn and uniformly chosen sk∈ {0,1} n. For 128-bits of semi-honest security, the Pool OPRF has an online communication cost of 11.9 kB, and a computational runtime of less than 3 ms on a single thread (via an open-source software implementation). This is more efficient (in either online communication cost or runtime) than constructions from well-known PQ PRFs, and is competitive even with constructions that only conjecture PQ security on lesser-known assumptions. As a result, our design gives high-performance, post-quantum variants of established OPRF applications in multi-party computation and private set operation protocols.

Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions’ range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.

We introduce ChalametPIR: a single-server Private Information Retrieval (PIR) scheme supporting fast, low-bandwidth keyword queries, with a conceptually very simple design. In particular, we develop a generic framework for converting PIR schemes for index queries over flat arrays (based on the Learning With Errors problem) into keyword PIR. This involves representing a key-value map using any probabilistic filter that permits reconstruction of elements from inclusion queries (e.g. Cuckoo filters). In particular, we make use of recently developed Binary Fuse filters to construct ChalametPIR, with minimal efficiency blow-up compared with state-of-the-art index-based schemes (all costs bounded by a factor of (≤ 1.08)). Furthermore, we show that ChalametPIR achieves runtimes and financial costs that are factors of between (6x)-(11x) and (3.75x)-(11.4x) more efficient, respectively, than state-of-the-art keyword PIR approaches, for varying database configurations. Bandwidth costs are additionally reduced or remain competitive, depending on the configuration. Finally, we believe that our application of Binary Fuse filters can have independent value towards developing efficient variants of related cryptographic primitives (e.g. private set intersection), that already benefit from using less efficient filter constructions.

We design FrodoPIR — a highly configurable, stateful, single-server Private Information Retrieval (PIR) scheme that involves an offline phase that is completely client-independent. Coupled with small online overheads, it leads to much smaller amortized financial costs on the server-side than previous approaches. In terms of performance for a database of 1 million 1KB elements, FrodoPIR requires < 1 second for responding to a client query, has a server response size blow-up factor of < 3.6x, and financial costs are ~$1 for answering 100,000 client queries. Our experimental analysis is built upon a simple, non-optimized Rust implementation, illustrating that FrodoPIR is particularly suitable for deployments that involve large numbers of clients.

Threshold aggregation reporting systems promise a practical, privacy-preserving solution for developers to learn how their applications are used in-the-wild. Unfortunately, proposed systems to date prove impractical for wide scale adoption, suffering from a combination of requiring: i) prohibitive trust assumptions; ii) high computation costs; or iii) massive user bases. As a result, adoption of truly-private approaches has been limited to only a small number of enormous (and enormously costly) projects.

The question at which layer network functionality is presented or abstracted remains a research challenge. Traditionally, network functionality was either placed into the core network, middleboxes, or into the operating system - but recent developments have expanded the design space to directly introduce functionality into the application (and in particular into the browser) as a way to expose it to the user.

Abstract The growth of content delivery networks (CDNs) has engendered centralized control over the serving of internet content. An unwanted by-product of this growth is that CDNs are fast becoming global arbiters for which content requests are allowed and which are blocked in an attempt to stanch malicious traffic. In particular, in some cases honest users-especially those behind shared IP addresses, including users of privacy tools such as Tor, VPNs, and I2P - can be unfairly targeted by attempted ‘catch-all solutions’ that assume these users are acting maliciously. In this work, we provide a solution to prevent users from being exposed to a disproportionate amount of internet challenges such as CAPTCHAs. These challenges are at the very least annoying and at their worst - when coupled with bad implementations - can completely block access from web resources. We detail a 1-RTT cryptographic protocol (based on an implementation of an oblivious pseudorandom function) that allows users to receive a significant amount of anonymous tokens for each challenge solution that they provide. These tokens can be exchanged in the future for access without having to interact with a challenge. We have implemented our initial solution in a browser extension named “Privacy Pass”, and have worked with the Cloudflare CDN to deploy compatible server-side components in their infrastructure. However, we envisage that our solution could be used more generally for many applications where anonymous and honest access can be granted (e.g., anonymous wiki editing). The anonymity guarantee of our solution makes it immediately appropriate for use by users of Tor/VPNs/ I2P. We also publish figures from Cloudflare indicating the potential impact from the global release of Privacy Pass.