The paper proposes a non-cryptographic, End-to-End Verifiable (E2E-V) voting scheme that achieves Software-Free Verification (SFV) by allowing voters to audit election integrity using only basic arith…

The paper introduces ACE, a novel voting protocol that achieves end-to-end verifiability and strong voter privacy by combining tally-hiding aggregation with an Audit-or-Cast challenge, eliminating the…

The paper proves that any voting rule based solely on wallet balance, including anti-plutocratic mechanisms like Quadratic Voting, is fundamentally vulnerable to Sybil attacks on permissionless blockc…

This paper analyzes the Loki e-voting protocol, demonstrating that while it attempts to solve coercion-resistance without pre-agreed secrets, it remains vulnerable to specific attacks, suggesting that…

Obscura is a novel, decentralized privacy protocol for the Algorand blockchain that achieves transaction anonymity using LSAG ring signatures, overcoming the limitations of existing zk-SNARK-based met…

This paper proposes a secure liquid democracy mechanism using sealed delegation and ranked multi-delegation with personal fallback ballots, and evaluates its impact on representational accuracy and vo…

This paper introduces a novel, efficient protocol for training Gradient Boosting Decision Trees (GBDT) on vertically partitioned data held by two mutually distrustful parties while ensuring complete a…

The paper proves that platform-deterministic inference is a necessary and sufficient condition for trustworthy AI, establishing that AI trust fundamentally relies on consistent arithmetic.

The paper introduces the Unicity Execution Layer, a secure, modular component that enables trustless off-chain transactions while guaranteeing double-spending prevention and enhancing user privacy.

The paper introduces $I$-$(OT)^2$, a novel base 1-out-of-2 Oblivious Transfer (OT) protocol designed to minimize computation and interaction for resource-constrained IoT devices.

Pepper is a novel, high-bandwidth anonymous broadcast protocol that achieves cryptographic sender anonymity and significantly improves messaging throughput compared to existing state-of-the-art system…

The paper proposes a secure and verifiable aggregation scheme for Federated Learning using a non-colluding dual-server architecture and linear tags, which significantly enhances user privacy and reduc…

The paper introduces a lightweight, sampling-based cryptographic protocol for verifiable AI inference that drastically reduces proving overhead from minutes to milliseconds by leveraging statistical p…

CHRONOS is a hardware-assisted framework that significantly reduces the latency of secure federated learning by decoupling cryptographic key setup from the active training phase, while maintaining hig…

The paper introduces a framework, PD-FHC, that allows users to outsource Boolean computations to an untrusted cloud while guaranteeing both computational privacy and plausible deniability against coer…

This paper proposes a novel secure aggregation protocol for Federated Learning using two-layer secret sharing, reducing communication and computational overhead.

This paper provides the first comprehensive cryptanalysis of the Legendre Pseudorandom Function over extension fields, demonstrating key recovery attacks under both passive and active threat models.

ZK-Value introduces a practical, scalable zero-knowledge system for calculating data valuations (Shapley values) in data marketplaces, significantly reducing proving time while maintaining high accura…

TAPAS introduces an efficient, asymmetric two-server private aggregation scheme that significantly reduces computational and communication costs for large-scale federated learning compared to existing…

SEAL-Tag is a privacy-preserving runtime environment that mitigates PII leakage in Retrieval-Augmented Generation (RAG) systems by enforcing verifiable evidence aggregation and structured auditing.