CS & research

arXiv:2610.31814v1 Announce Type: new Abstract: Permissioned blockchains authenticate transactions, votes and quorum certificates with computationally secure signatures, and post-quantum replacements inflate consensus traffic by orders of magnitude. Information-theoretic alternatives have required a trusted authority, quantum channels, per-signature interaction, or single-use keys with bounded transfer. We present SILMARILS-MV, a multi-verifier unconditionally secure signature in which a signature is the vector $\alpha = A\,e(m)$ and each verifier holds a secret functional $c_i = \Gamma^T \lambda(x_i)$ derived from virtual anchors $\Gamma$ that nobody learns. Against unbounded adversaries corrupting up to $\omega$ verifiers and any signers, and observing every decision, an honest verifier accepts a forged or invalid signature with probability at most $Q/q + 1/(q-1)$, a bound that is tight in its leading term. The scheme is strongly unforgeable, non-repudiable, transferable across unboundedly many hops, and linearly aggregatable. Keys are issued by the validators through perfectly secure MPC for $t < n/3$, without broadcast. To scale, we let the committee itself sign: in threshold n…

arXiv:2610.32626v1 Announce Type: new Abstract: Vision-based image classification models benefit from Federated Learning (FL) across vehicles, however learning accurate models from distributed vehicular cameras is challenging due to data heterogeneity and privacy concerns. To improve performance on non-IID data, advanced FL optimization approaches have emerged that share auxiliary insights, such as client gradient behaviors. While these gradient-guided optimized FL methods improve the model accuracy, their reliance on sharing additional gradient information introduces a previously unexplored privacy vulnerability. In our empirical privacy-leakage experiment, we show that a malicious server can infer clients' label sets from shared guidance matrices under heterogeneous client label distributions (simulated via Dirichlet-based label partitions), between 50% and 84% accuracy. To address this privacy leakage, we propose PPFLex that replaces the central server with three Secure Multi-Party Computation (MPC) servers to securely compute the global guidance matrix and perform federated averaging under semi-honest and malicious MPC server assumptions. We evaluate PPFLex on widely used traff…

arXiv:2610.32661v1 Announce Type: new Abstract: Machine-learning research on water cyber-physical systems (CPS) is constrained by datasets that are either real but chemically poor, or simulated but free of sensing noise and attacks. We present AquaSense-ICS, an open dataset and benchmark that co-registers (i) a 25.4 h recording window (8.0 h of recorded data, 26% coverage) from a Raspberry Pi 5 edge node (TDS probe and BME680 ambient sensor, 1-2 s sampling, 23,688 records), with (ii) a looped network of 13 nodes (10 junctions, 2 reservoirs, 1 tank) simulated in EPANET-MSX for six water-quality species, (iii) a parameterised library of cyber-attacks (false data injection, replay, denial of service, drift) and unintentional sensor faults with sample-level ground truth, and (iv) augmented and TimeGAN-generated sequences, flagged by an origin column. The attack, fault and water-quality labels are traceable to a seeded generator, a documented rule or a simulation input. We show that the most common way of benchmarking such hybrid data, random row splits and labels computed from the input features, yields near-perfect but uninformative scores (macro-F1 = 0.9998 for Random Forest), and we…

arXiv:2610.32803v1 Announce Type: cross Abstract: The consensus protocols of most high-performance blockchains use quorum certificates, which bundle the signatures of a quorum of validators. Signature schemes such as BLS can aggregate the signatures of a whole quorum into a single short signature, which keeps these certificates small. A large-scale quantum computer would break these schemes, and no post-quantum scheme with comparable aggregate size and aggregation time is known. Without aggregation, a certificate holds one large post-quantum signature per member of the quorum, so it becomes infeasible for each validator to broadcast certificates, as modern consensus protocols often require. We present Quantum Cadence, a post-quantum consensus protocol with multiple concurrent proposers that avoids certificates altogether: every validator counts quorums among the messages it has received itself. Its safety and liveness do not even depend on signatures, although we add signatures to obtain accountability. Quantum Cadence builds on Cadence and replaces its slot consensus, Chorus, with Quantum Chorus, which keeps the optimal fast-path latency of Chorus: dissemination followed by two ro…

arXiv:2603.19781v3 Announce Type: replace Abstract: Lilliput is a lightweight block cipher based on a Type-2 Extended Generalized Feistel Network (EGFN). Although it has been studied under conventional cryptanalytic techniques, its resistance to fault attacks remains largely unexplored. We present a theoretical differential fault analysis of Lilliput under a random-location nibble fault model. A nonzero nibble fault is injected at the input to round 27, with the injection location uniformly distributed among the 16 state branches and unknown to the adversary. We analyze fault propagation through the EGFN and derive ciphertext-difference conditions to identify the faulty branch. A DDT-weighted calculation yields an overall branch-identification accuracy of approximately 99.9980% under independent uniform sampling of plaintexts, nonzero fault values, and fault locations; simulations of 2^20 random faults give 99.9983%. After identifying the faulty branch, we classify propagation patterns using the injected difference and the intermediate S-box output difference. We then derive DDT-based constraints on last-round and penultimate-round subkeys and intersect candidate sets from multiple…

arXiv:2610.21245v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) enables large language models to leverage external knowledge but introduces privacy risks when the retrieved corpus contains sensitive information. Differential privacy (DP) offers formal protection, yet interpreting and comparing privacy guarantees across existing DP-RAG methods remains challenging. In particular, a privacy budget reported for a single response does not directly characterize privacy loss over repeated queries, and even identical values of $\varepsilon$ may represent different guarantees depending on the protected data unit, accounting horizon, and observable outputs. This survey critically examines how privacy accounting evolves from single-query generation to multi-query RAG systems. We categorize existing approaches into query-time privatization, multi-query accounting, and one-time corpus privatization. We analyze their underlying assumptions and privacy--utility trade-offs. Rather than ranking methods by $\varepsilon$ alone, we propose a framework that interprets a DP-RAG guarantee along three dimensions: privacy granularity, accounting scope, and release boundaries. We co…

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