Cooperative Intelligent Transportation Systems (C-ITS) require anonymous authentication and encryption schemes to ensure secure communication while preserving vehicle privacy. Zone Encryption (ZE) (Camenisch et al., EuroS&P'20) provides such a mechanism tailoredf or vehicular networks. To prevent misuse of anonymity, these schemes include revocation mechanisms to remove misbehaving vehicles from networks. However, revocation typically relies on trusted third-party (TTP) authorities to trace and identify malicious vehicles, raising concerns of continuous tracking and profiling by them. This creates a challenging dynamic between privacy and accountability against honest-but-curious authorities.

The anonymity, multi-show unlinkability, and selective disclosure property of anonymous credentials enables users to access third-party services without revealing unnecessary details or being profiled. Threshold-based anonymous credentials provide a distributed framework to issue these credentials. While all of this enhances privacy, anonymity can also be misused and some schemes, therefore, have an opening mechanism, which enables the authorities to trace a user's identity. Nevertheless, relying solely on this measure is inadequate as the users can continue to use their previously issued anonymous credentials to authenticate themselves successfully with the service providers. To address this issue, we propose a revocation mechanism for such schemes using dynamic threshold accumulators (DTA) (Helminger et al. 2021). We first formally define a generic threshold-based anonymous credentials with an opening scheme (TACO) and subsequently propose an extension of TACO, "Revocable TACO (RTACO)" - revocable threshold-based credentials over blockchains - that integrates a revocation mechanism based on DTAs to the TACO system. In RTACO, we integrate a revocation handle into the credential as an extra attribute, allowing the disclosure of this attribute during the opening phase, which is then used to blocklist the credential, preventing its further use. We formally prove the security of this scheme in the universal composability (UC) framework. We also give a proof-of-concept implementation of RTACO over the Ethereum blockchain.

This paper introduces the corpus for the novel task of presupposition generation - a natural language generation problem where a model produces a list of presuppositions carried by the given input sentence, in the context of the presented research - given the cross-examination question. Two datasets, PECaN (Presupposition, Entailment, Contradiction and Neutral) and PGen (Presuppostion Generation), are designed to fine-tune existing BERT (CITATION) and T5 (CITATION) models for classification and generation tasks. Various corpora construction methods are proposed ranging from manual annotations, prompting the GPT 3.0 model, to augmenting data from the existing corpora. The fine-tuned models achieved high accuracy on the novel Presupposition as Natural Language Inference (PNLI) task which extends the traditional Natural Language Inference (NLI) incorporating instances of presupposition into classification. T5 outperforms BERT by broad margin achieving an overall accuracy of 84.35% compared to 71.85% of BERT, and specifically when classifying presuppositions (93% vs 73% respectively). Regarding presupposition generation, we observed that despite the limited amount of data used for fine-tuning, the model displays an emerging proficiency in generation presuppositions reaching ROUGE scores of 43.47, adhering to systematic patterns that mirror valid strategies for presupposition generation, although failed to generate the complete lists.

We consider the uplink of a Selective User-Forwarded Cell-free massive MIMO (SUF-CF-mMIMO) system, where each access point (AP) forwards the received symbols of only a selected subset of users to the centralized processing unit (CPU) for coherent combining. SUF-CF-mMIMO gives significant savings in fronthaul signaling compared to the conventional CF-mMIMO. This paper investigates the uplink performance of SUF-CF-mMIMO with one-bit quantized symbols sent over the fronthaul to the CPU by each AP. Novel expressions for spectral efficiency (SE) are derived for the uplink in the presence of quantization distortion. Simulation results show that SUF-CF-mMIMO is more robust to quantization effects compared to the conventional CF-mMIMO.

Signature-based algorithms have brought large improvements in the performances of Gröbner bases algorithms for polynomial systems over fields. Furthermore, they yield additional data which can be used, for example, to compute the module of syzygies of an ideal or to compute coefficients in terms of the input generators.