Fair and free elections are the foundation of democracies and democratic processes. They require voting protocols that guarantee the integrity and verifiability of the result, as well as the private choice of each voter. Currently deployed e-voting protocols rely on traditional hardness assumptions, like the discrete logarithm problem, to provide these security guarantees. They are not post-quantum secure (pq-secure). While first proposals for pq-secure protocols exist, they are limited in the variety of voting scenarios they can support and/or in terms of efficiency. In this work, we therefore propose PQKryvos, an efficient and flexible pq-secure homomorphic e-voting protocol that can be instantiated for a wide variety of election methods and ballot formats. Our construction efficiently combines homomorphic lattice-based commitments with hash-based general-purpose proofs (GPZKPs) to ensure ballot correctness. As a pq-secure instantiation of the Kryvos framework introduced by Huber et al. (CCS 2022), PQKryvos not only provides voter privacy and (public) verifiability of the result, but additionally allows for the stronger privacy notion of public tally-hiding. Public tally-hiding ensures that only the intended election result (such as the full vote count or only the winner) is publicly revealed, while no additional information is leaked. This further improves the privacy for both voters and election candidates. PQKryvos is the first pq-secure e-voting protocol to generically support arbitrary ballot formats and the first to provide public tally-hiding. Our implementation and evaluation of PQKryvos demonstrate that it achieves practical performance for diverse election schemes and outperforms the original pre-quantum Kryvos instantiation in some settings. Moreover, we demonstrate that by utilizing GPZKPs, existing pq-secure e-voting protocols can support additional ballot formats, can be enhanced in their tallying phase, and can be extended to publicly tally-hiding protocols.

Voter privacy and end-to-end (E2E) verifiability are critical features of electronic voting (e-voting) systems to safeguard elections. To achieve these properties commonly a perfect bulletin board (BB) is assumed that provides consistent, reliable, and tamper-proof storage and transmission of voting data. However, in practice, BBs operate in asynchronous and unreliable networks, and hence, are susceptible to vulnerabilities such as equivocation attacks and dropped votes, which can compromise both verifiability and privacy. Although prior research has weakened the perfect BB assumption, it still depends on trusting certain BB components. In this work, we present and initiate a formal exploration of designing e-voting systems based on fully untrusted BBs. For this purpose, we leverage the notion of accountability and in particular use accountable BBs. Accountability ensures that if a security breach occurs, then cryptographic evidence can identify malicious parties. Fully untrusted BBs running in asynchronous networks bring new challenges. Among others, we identify several types of attacks that a malicious but accountable BB might be able to perform and propose a new E2E verifiability notion for this setting. Based on this notion and as a proof of concept, we construct the first e-voting system that is provably E2E verifiable and provides vote privacy even when the underlying BB is fully malicious. This establishes an alternative to traditional e-voting architectures that rely on (threshold) trusted BB servers.

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving participant privacy. Decentralized FL (DFL) eliminates reliance on a central server, mitigating the single point of failure inherent in the traditional FL paradigm, while introducing deployment challenges on resource-constrained devices. To evaluate real-world applicability, this work designs and deploys a physical testbed using edge devices such as Raspberry Pi and Jetson Nano. The testbed is built upon a DFL training platform, NEBULA, and extends it with a power monitoring module to measure energy consumption during training. Experiments across multiple datasets show that model performance is influenced by the communication topology, with denser topologies leading to better outcomes in DFL settings.

Elections are an important corner stone of democratic processes. In addition to publishing the final result (e.g., the overall winner), elections typically publish the full tally consisting of all (aggregated) individual votes. This causes several issues, including loss of privacy for both voters and election candidates as well as so-called Italian attacks that allow for easily coercing voters.