secure multiparty computation

In distributed multi-party secure computation, achieving both security and efficiency becomes challenging when the number of adversaries exceeds half the total participants (t ≥ n/2). Method: This paper proposes a differentially private secure multiplication protocol over the real numbers. Its core innovation is a novel “noise-layered encoding” mechanism, where multiple layers of controlled random noise are superimposed to ensure adversarial indistinguishability, while honest parties can decode layer-by-layer to recover exact results. Contributions/Results: First, it formally models the accuracy–privacy trade-off inherent in differential privacy. Second, it proves that only t+1 parties suffice to tolerate t colluding adversaries—breaking the classical information-theoretic requirement of 2t+1 parties (e.g., BGW). Third, it provides rigorous ε-differential privacy guarantees with bounded accuracy loss, while significantly reducing communication and computational overhead compared to prior approaches.

This paper addresses secure multiparty computation (MPC) in settings devoid of digital devices, proposing a fully physical paradigm for Boolean function evaluation using mechanical balances and coins. Methodologically, it pioneers the integration of mechanical equilibrium principles into cryptography, combining coin-based encoding, physical zero-knowledge proofs, Boolean function decomposition, and mapping to balance operation sequences to construct four complete, composable protocols. Contributions include: (1) information-theoretically secure collaborative evaluation of arbitrary n-variable Boolean functions; (2) elimination of all communication and digital dependencies—achieving zero communication complexity; (3) no reliance on trusted third parties, enabling manual execution and pedagogical demonstration; and (4) overcoming expressiveness limitations of prior card-based protocols by establishing the first general-purpose, scalable physical security computing framework.

Prior work lacks a systematic analysis linking commitment scheme (CS) security properties to the robustness, privacy, and composable security of multi-party computation (MPC) protocols. Method: This paper establishes, for the first time, a formal mapping between CS types—including Pedersen, Fiat-Shamir, and universally composable (UC)-secure CS—and MPC construction paradigms (e.g., GMW, BGW, SPDZ), adversarial models, and application contexts. It proposes functional and security-driven CS selection criteria and conducts cryptographic analysis alongside cross-framework evaluation to quantify CS impact on MPC efficiency, resilience against adaptive attacks, and composability. Contribution/Results: The work bridges theoretical security guarantees with practical protocol design, yielding principled guidelines for CS instantiation in MPC. It provides both theoretical foundations and actionable insights for building robust, deployable privacy-preserving systems.

This work addresses the challenge of simultaneously achieving weak-link tolerance and low computational overhead in high-throughput secure multi-party computation (MPC) over heterogeneous networks. We propose efficient ring-based three-party (semi-honest) and four-party (malicious, single-corruption) protocols. Our approach integrates ring-based secret sharing, lightweight arithmetic circuit optimization, and a high-throughput C++ implementation. While preserving optimal communication complexity—3 ring elements per multiplication for 3PC and 5 for 4PC—we achieve, for the first time, dual breakthroughs in weak-link robustness and computational efficiency: halving the number of gate instructions and improving throughput in heterogeneous networks by up to 8×. Experiments on a 25 Gbit/s LAN show our 5/6 protocol attains >10⁹ 32-bit multiplications/sec or 3.2×10¹⁰ AND gates/sec—outperforming mainstream frameworks such as MP-SPDZ by 2–3 orders of magnitude—and set new records for throughput in both 3PC and 4PC.

In secure two-party computation (2PC), existing protocols for real-valued functions impose strict input constraints (|x| < L/3), severely limiting practical applicability. This paper proposes a general framework based on signed integer secret sharing, extending the supported input range to |x| < B (where B ≤ L/2)—encompassing the natural representation interval of the integer ring modulo L. Our approach introduces efficient signed integer–real encoding/decoding, high-precision polynomial approximation, and optimized secure evaluation protocols. For the first time, it enables secure computation of nonlinear operations—including integer division, trigonometric functions, and exponentials—without any additional input constraints. Experiments demonstrate that our protocol reduces communication overhead for e⁻ˣ evaluation to 31% of SirNN and Bolt, achieves 5.53× and 3.09× higher throughput, and attains a maximum ULP error of only 1.435—significantly outperforming state-of-the-art methods in both accuracy and efficiency.

This work proposes a novel quantum optical bit commitment protocol to address the fundamental impossibility of unconditionally secure bit commitment in classical settings. By introducing a phase-encoding mechanism and leveraging a physical-layer security assumption—namely, that the network provider ensures communication channels are immune to eavesdropping—the protocol circumvents the well-known Mayers attack under the honest-but-curious model. The authors rigorously establish its security through information-theoretic analysis, thereby providing a provably secure building block for privacy-preserving distributed AI computation. This advancement enables secure multiparty computation with strong guarantees for data confidentiality, marking a significant step toward practical implementations of secure function evaluation in distributed environments.

This work addresses the challenge of achieving privacy-preserving exact summation of Boolean bitstreams in multi-party settings while defending against inference attacks by both servers and aggregators. The authors propose PolyVeil, a secure aggregation protocol that encodes private bits as permutation matrices within the Birkhoff polytope, leveraging a two-tier architecture. Notably, PolyVeil operates without public-key infrastructure and uniquely integrates the geometric structure of the Birkhoff polytope with differential privacy. It outputs exact sums while guaranteeing perfect simulation-based security against the server and rendering likelihood inference for the aggregator #P-hard. The full variant achieves zero statistical distance security, whereas a compressed variant offers non-trivial ε-differential privacy under moderate signal-to-noise ratios, all with only O(k) communication complexity.

Implementation flaws in differential privacy (DP) can be maliciously exploited to mount stealthy attacks. Method: We propose the Gaussian Pancake Mechanism (GPM), a novel mechanism computationally indistinguishable from the standard Gaussian mechanism yet provably offering significantly weaker privacy guarantees. GPM systematically degrades the effective privacy budget ε by carefully engineering its noise distribution while preserving statistical indistinguishability under standard DP audits. Contribution/Results: GPM constitutes the first evadable backdoor attack against DP systems. We formally prove its computational indistinguishability from the Gaussian mechanism under standard cryptographic assumptions. Empirical evaluation demonstrates that GPM evades detection across diverse auditing strategies—including statistical tests, privacy loss estimation, and implementation-level checks—while inducing substantial real-world privacy leakage. This work exposes critical risks posed by implementation-layer vulnerabilities in open-source DP libraries and underscores the necessity of rigorous formal verification and systematic auditing of DP deployments.

We present CryptoChoreo, a choreography language for the specification of cryptographic protocols. Choreographies can be regarded as an extension of Alice-and-Bob notation, providing an intuitive high-level view of the protocol as a whole (rather than specifying each protocol role in isolation). The extensions over standard Alice-and-Bob notation that we consider are non-deterministic choice, conditional branching, and mutable long-term memory. We define the semantics of CryptoChoreo by translation to a process calculus. This semantics entails an understanding of the protocol: it determines how agents parse and check incoming messages and how they construct outgoing messages, in the presence of an arbitrary algebraic theory and non-deterministic choices made by other agents. While this semantics entails algebraic problems that are in general undecidable, we give an implementation for a representative theory. We connect this translation to ProVerif and show on a number of case studies that the approach is practically feasible.

This work addresses the challenge of computing the product of arbitrary M private inputs across a distributed system of N nodes while tolerating up to T colluding adversaries and guaranteeing ε-differential privacy, where perfect privacy and accuracy are inherently incompatible. The authors propose a differentially private secure multiplication framework based on coded polynomials and hierarchical noise injection, extending the privacy–accuracy trade-off theory from the two-input case to arbitrary M. By systematically canceling lower-order noise terms, the framework significantly improves estimation accuracy. The study characterizes the optimal privacy–accuracy trade-off for node counts satisfying (M−1)T+1 ≤ N ≤ MT, and for the minimal setting N = T+1, establishes asymptotically tight achievability and converse bounds in the high-privacy regime.