相关论文: Plaintext-Scale Fair Data Exchange
This brief presents a novel IEEE 802.16e (WiMAX) based decoder that performs close to the 5G code but without the expensive hardware re-development cost. The design uses an extension of the existing WiMAX parity check code to reduce the…
In federated learning (FL), data providers jointly train a machine learning model without sharing their training data. This makes it challenging to provide verifiable claims about the trained FL model, e.g., related to the employed training…
Paramount to vehicle safety, broadcasted Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) are pseudonymously authenticated for security and privacy protection, with each node needing to…
As large language models (LLMs) are increasingly deployed for software engineering, constructing high-quality benchmarks is crucial for evaluating not just the functional correctness, but also the formal verifiability of generated code.…
Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks.…
The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a modular, zero-shot MLLM pipeline for open-domain, text-image…
Ephemeral Diffie-Hellman Over COSE (EDHOC) aims at being a very compact and lightweight authenticated Diffie-Hellman key exchange with ephemeral keys. It is expected to provide mutual authentication, forward secrecy, and identity…
Fully Homomorphic Encryption (FHE) enables operations on encrypted data, making it extremely useful for privacy-preserving applications, especially in cloud computing environments. In such contexts, operations like ranking, order…
We suggest using Fully Homomorphic Encryption (FHE) to be used, not only to keep the privacy of information but also, to verify computations with no additional significant overhead, using only part of the variables length for verification.…
Blockchain technology promises a decentralized, trustless, and interoperable infrastructure. However, widespread adoption remains hindered by issues such as limited scalability, high transaction costs, and the complexity of maintaining…
Current blockchain consensus protocols -- notably, Proof of Work (PoW) and Proof of Stake (PoS) -- deliver global agreement but exhibit structural constraints. PoW anchors security in heavy computation, inflating energy use and imposing…
Public blockchains provide a decentralized method for storing transaction data and have many applications in different sectors. In order for users to track transactions, a simple method is to let them keep a local copy of the entire public…
Initiated from Nakamoto's Bitcoin system, blockchain technology has demonstrated great capability of building secure consensus among decentralized parties at Internet-scale, i.e., without relying on any centralized trusted party. Nowadays,…
Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme…
Financial Generative Pre-trained Transformers (FinGPT) with multimodal capabilities are now being increasingly adopted in various financial applications. However, due to the intellectual property of model weights and the copyright of…
We describe initial work on an extension of the Kaldi toolkit that supports weighted finite-state transducer (WFST) decoding on Graphics Processing Units (GPUs). We implement token recombination as an atomic GPU operation in order to fully…
The advent of quantum computing threatens the security of traditional encryption algorithms, motivating the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized…
Though rectified flow models have achieved remarkable performance in image, video, and 3D generation, their practical deployments are challenged by slow inference speeds. Prior acceleration methods reuse cached features from previous steps,…
In this paper, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to any obstruction by the malicious model holder during the…
We introduce an open-source GPU-accelerated fully homomorphic encryption (FHE) framework CAT, which surpasses existing solutions in functionality and efficiency. \emph{CAT} features a three-layer architecture: a foundation of core math, a…