Always on Voting: A Framework for Repetitive Voting on the Blockchain

Sarad Venugopalan; Ivana Stančíková; Ivan Homoliak

Home/Archive/arXiv

arXiv · 2107.10571

Always on Voting: A Framework for Repetitive Voting on the Blockchain

Sarad Venugopalan·Ivana Stančíková·Ivan Homoliak

View original source ↗Read source document ↗Export metadata ↓

Abstract

Elections repeat commonly after a fixed time interval, ranging from months to years. This results in limitations on governance since elected candidates or policies are difficult to remove before the next elections, if needed, and allowed by the corresponding law. Participants may decide (through a public deliberation) to change their choices but have no opportunity to vote for these choices before the next elections. Another issue is the peak-end effect, where the judgment of voters is based on how they felt a short time before the elections. To address these issues, we propose Always on Voting (AoV) -- a repetitive voting framework that allows participants to vote and change elected candidates or policies without waiting for the next elections. Participants are permitted to privately change their vote at any point in time, while the effect of their change is manifested at the end of each epoch, whose duration is shorter than the time between two main elections. To thwart the problem of peak-end effect in epochs, the ends of epochs are randomized and made unpredictable, while preserved within soft bounds. These goals are achieved using the synergy between a Bitcoin puzzle oracle, verifiable delay function, and smart contracts.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Sarad Venugopalan, Ivana Stančíková, Ivan Homoliak. 2023-09-24. Always on Voting: A Framework for Repetitive Voting on the Blockchain. https://arxiv.org/abs/2107.10571

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MIRANDA: short signatures from a leakage-free full-domain-hash scheme

We present $\mathsf{Miranda}$, the first family of full-domain-hash signatures based on matrix codes. This signature scheme fulfils the paradigm of Gentry, Peikert and Vaikuntanathan ($\mathsf{GPV}$), which gives strong security guarantees. Our trapdoor is very simple and generic: if we propose it with matrix codes, it can actually be instantiated in many other ways since it only involves a subcode of a decodable code (or lattice) in a unique decoding regime of parameters. Though $\mathsf{Miranda}$ signing algorithm relies on a decoding task where there is exactly one solution, there are many possible signatures given a message to sign and we ensure that signatures are not leaking information on their underlying trapdoor by means of a very simple procedure involving the drawing of a small number of uniform bits. In particular $\mathsf{Miranda}$ does not use a rejection sampling procedure which makes its implementation a very simple task contrary to other $\mathsf{GPV}$-like signatures schemes such as $\mathsf{Falcon}$ or even $\mathsf{Wave}$. We instantiate $\mathsf{Miranda}$ with the famous family of Gabidulin codes represented as spaces of matrices and we study thoroughly its security (in the EUF-CMA security model). For~$128$ bits of classical security, the signature sizes are as low as~$90$ bytes and the public key sizes are in the order of~$2.6$ megabytes.

cs.CR↗

arXiv2026

SteganoBackdoor: Evading Data-Poisoning Defenses via Steganographic Backdoors

Transformer-based models are highly susceptible to backdoor attacks via supervised fine-tuning (SFT). To red-team existing data-poisoning defenses, prior work has increasingly focused on stylized triggers, synthetic artifacts, and token-level perturbations designed to evade detection. However, this trend has shifted threat models away from naturally occurring semantic triggers and realistic low-budget poisoning settings. Addressing this gap, we introduce SteganoBackdoor, an optimization-based framework that transforms semantic-trigger seeds through autoregressive token replacement, sequentially minimizing embedding overlap with the inference-time trigger while preserving a strong per-sample training-time payload. The resulting SteganoPoisons maintain linguistic fluency and encode the payload across ordinary tokens, such that no individual token carries a concentrated signal and the full payload instead emerges from their exact combination and ordering. Across 18 encoder-based and decoder-only models spanning 120M to 14B parameters, SteganoBackdoor achieves high attack success under sub-percent poisoning budgets and exposes limitations in existing data-poisoning defenses.

cs.CR↗

arXiv2026

Foundations and Design Principles of Lightweight Cryptography for IoT Systems

The successful deployment of the Internet of Things (IoT) applications relies heavily on their robust security, and lightweight cryptography is considered an emerging solution in this context. While existing surveys have been examining lightweight cryptographic techniques from the perspective of hardware and software implementations or performance evaluation, there is a significant gap in addressing different security aspects, such as design principles, specific to the IoT environment. This study aims to bridge this gap. This research presents an examination with focusing on the security evaluation of symmetric lightweight ciphers commonly used in IoT systems. The objective of this study is to provide a concise overview of lightweight ciphers with emphasizing on their security challenges which is an essential consideration for real-time and resource-constrained applications.

cs.CR↗