~ similar to 2604.14909v1· 20 results
The paper proposes a new DDH-based technique that significantly reduces the key size of multi-party Distributed Point Function (DPF) secret sharing schemes, achieving an $O( oot{3}{N})$ key size for h…
Fengxia Liu, Zixian Gong, Kun Tian, Yi Zhang +2 more
The paper introduces a unified framework for Quantum Fully Homomorphic Encryption (QFHE) that achieves exponential efficiency improvements by integrating a novel modular arithmetic program (MAP) tailo…
The paper introduces a novel public key encryption scheme with high security by leveraging the conjectured intractability of two types of highly corrupted constraint satisfaction problems (CSPs).
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…
The paper proposes a novel, perfectly secure Information-Theoretic Distributed Point Function (ITDPF) that converts point functions into shares using asymptotically shorter secret keys compared to exi…
The paper proposes a novel ring-based information-theoretic Private Information Retrieval (itED-PIR) scheme that overcomes the key size and communication overhead limitations of existing field-based A…
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.
Jian Ding, Cheng Wang, Hongju Li, Cheng Shu +1 more
The paper introduces a novel, asymptotically ideal Conjunctive Hierarchical Secret Sharing (CHSS) scheme using the Chinese Remainder Theorem (CRT) for polynomial rings, achieving high security and an…
The paper proposes a provably secure, single-round two-party computation protocol for approximate matrix multiplication using lattice-based cryptography, demonstrated for secure control law implementa…
This paper provides a comparative analysis and benchmarking of Secure Multi-Party Computation (SMPC) and Fully Homomorphic Encryption (FHE) for machine learning, finding that the optimal choice depend…
The paper proposes a novel space switching method to efficiently unify arithmetic and comparison operations within Fully Homomorphic Encryption (FHE) schemes, achieving significant performance improve…
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.
The paper analyzes the structured CVP distance on the log-unit lattice of cyclotomic fields, significantly reducing the conjectured CDPR factor for the ML-KEM cryptosystem from exponential to sub-poly…
The paper introduces PolyVeil, a protocol for private Boolean summation that uses permutation matrices in the Birkhoff polytope, achieving strong security guarantees while highlighting a fundamental t…
Hongju Li, Jian Ding, Fuyou Miao, Cheng Wang +1 more
The paper proposes a novel CRT-based asymptotically perfect Disjunctive Hierarchical Secret Sharing (DHSS) scheme that overcomes security and information rate limitations of existing methods.
This paper establishes a complexity hierarchy for shuffle operations used in card-based cryptography, classifying them by implementation difficulty and proving separations between these levels.
The paper introduces Sherpa.ai, a multi-party Private Set Union (PSU) protocol that enables privacy-preserving entity alignment for Vertical Federated Learning (VFL) without disclosing shared sample i…
The paper establishes a universal, machine-checked 1-Bit Barrier for the internal wire map of masked Barrett reduction, providing a strong side-channel leakage bound for post-quantum cryptography.
This paper extends quantum lattice reduction techniques (CDPR) from ideal to module lattices over cyclotomic rings, achieving a constant module reduction factor and providing a rigorous, bounded-preci…
Divesh Aggarwal, Rishav Gupta, Hai Hoang Nguyen, Kel Zin Tan +1 more
The paper presents a new worst-case to average-case reduction for the Learning Parity with Noise (LPN) problem, achieving hardness for inverse-polynomial noise rates previously unattainable.