Secure Multi-Party Computation

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Secure multi-party computation (MPC) enables parties to compute a shared result while keeping their individual inputs hidden, supporting collaboration where data cannot be pooled. Common approaches use secret sharing, garbled circuits, function secret sharing, or combinations with encryption to perform tasks such as private aggregation, machine-learning inference, and database retrieval. Current work co-designs protocols, model operations, and hardware to reduce communication, preprocessing, and runtime, often using quantization or specialized operations. A central challenge is balancing practical speed and accuracy with strong guarantees against malicious participants, dropouts, and information leakage.

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20 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design
Sep 15, 2026Jiangrui Yu, Ye Yu, Si Chen +5Efficient Neural Network Inference Secure Multi-Party Computation
Peking University, Beijing, China · Open Security Research, Shenzhen, China · Tsinghua University, Beijing, China
Spruce: Scalable Private Outsourced Retrieval Using Compact Embeddings
Sep 3, 2026Peichun Hua, Yunming XiaoSecure Multi-Party Computation Embedding Compression
The Chinese University of Hong Kong, Shenzhen · State Key Laboratory of Internet Architecture, Tsinghua University
Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation
Aug 20, 2026Ş. Selcan Magara, Esther Havemann, Debora Jutz +2Privacy-Preserving ML Secure Multi-Party Computation
Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany · Medical Data Privacy and Privacy-Preserving Machine Learning, University of Tübingen, Tübingen, Germany · Lab for Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany +1
MOSAIC: Masked Outsourcing of Secure AI Computations
Jul 31, 2026James Hsin-yu Chiang, Sheila Zingg, Kari Kostiainen +1Efficient Transformer Inference Secure Multi-Party Computation
ETH Zurich Switzerland
Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries
Jul 23, 2026Hyeong-Gun Joo, Songnam Hong, Dong-Joon ShinSecure Multi-Party Computation Federated Learning Aggregation
Department of Electronic Engineering, Hanyang University, Seoul, South Korea
CILC: Cryptographically-secure Inter-agent Loop Closure Candidate Detection for Multi-Agent Collaborative SLAM
Jul 7, 2026Andrew Fishberg, Yixuan Jia, Jonathan P. HowSimultaneous Localization and Mapping Secure Multi-Party Computation
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
Privacy-Preserving Robustness Verification for Neural Networks
Jul 6, 2026Nianyun Song, Xiaokun Luan, Yu Guo +3Secure Multi-Party Computation Neural Network Verification
School of Artificial Intelligence, Beijing Key Laboratory of Artificial Intelligence for Education, Engineering Research Center of Intelligent Technology and Educational Application (Ministry of Education), Beijing Normal University, Beijing, China · School of Mathematical Sciences, Peking University, Beijing, China · School of Computer Science, University of Bristol, Bristol, UK
Giskard : Byzantine Robust and Private Aggregation for Large-Scale Decentralized Learning
Jun 17, 2026Ousmane Touat, César Sabater, Mohamed Maouche +1Secure Multi-Party Computation Communication-Efficient Distributed Training
INSA Lyon, LIRIS, CNRS, Villeurbanne, France · INRIA, INSA Lyon, Villeurbanne, France
MPC-Patch-Bench: Security-Aware LLM Code Patch for Multi-Party Computation
Jun 9, 2026Yukuan Zhang, Mengxin Zheng, Qian LouSecure Multi-Party Computation LLM Evaluation
University of Central Florida
FuseFSS: Efficient Secure LLM Inference with Function Secret Sharing
Jun 8, 2026Yuhan Ma, Yong Li, Stefan SchmidSecure Multi-Party Computation Privacy-Preserving Language Models
Huawei Heisenberg Research Center, Huawei Technologies Düsseldorf GmbH, Düsseldorf, Germany · Technische Univer-sität Berlin, Berlin, Germany
Practical Anonymous Two-Party Gradient Boosting Decision Tree
May 26, 2026Chenyu Huang, Fan Zhang, Minxin Du +6Secure Multi-Party Computation Gradient-Boosted Decision Trees
Tencent · Hong Kong Polytechnic University · Chinese University of Hong Kong +1
Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
May 20, 2026Sebastian Gruber, Tobias Harzfeld, Christoph G. Schuetz +2Secure Multi-Party Computation Distributed Optimization
Johannes Kepler University Linz, Linz, Austria · AIT Austrian Institute of Technology, Vienna, Austria
Privacy-Preserving LLMs Routing
Apr 17, 2026Xidong Wu, Yukuan Zhang, Yuqiong Ji +3Secure Multi-Party Computation Privacy-Preserving ML
Department of Electrical and Computer Engineering, University of Pittsburgh · Department of Computer Science, University of Central Florida · Department of Computer and Information Science, University of Pennsylvania +2
SecureRouter: Encrypted Routing for Efficient Secure Inference
Apr 16, 2026Yukuan Zhang, Mengxin Zheng, Qian LouEfficient Transformer Inference Secure Multi-Party Computation
University of Central Florida · Orlando, Florida, USA
Combinatorial Privacy: Private Multi-Party Bitstream Grand Sum by Hiding in Birkhoff Polytopes
Mar 24, 2026Praneeth VepakommaSecure Multi-Party Computation Secure Aggregation
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · Massachusetts Institute of Technology (MIT)
TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
Mar 20, 2026Harish Karthikeyan, Antigoni PolychroniadouSecure Aggregation Secure Multi-Party Computation
JPMorgan AI Research, JPMorgan AlgoCRYPT CoE
Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap
Mar 13, 2026Zakia Zaman, Praveen Gauravaram, Mahbub Hassan +2Privacy-Preserving ML Internet of Things
School of CSE, University of NSW, AU · TCS Research & Innovation, Tata Consultancy Services (Australia & New Zealand), Australia
UFO: Unlocking Ultra-Efficient Quantized Private Inference with Protocol and Algorithm Co-Optimization
Feb 21, 2026Wenxuan Zeng, Chao Yang, Tianshi Xu +4Secure Multi-Party Computation Convolutional Neural Networks
Institute for Artificial Intelligence, Peking University, China · School of Software and Microelectronics, Peking University, China · School of Integrated Circuits, Peking University, China +1
LRD-MPC: Efficient MPC Inference through Low-rank Decomposition
Feb 16, 2026Tingting Tang, Yongqin Wang, Murali AnnavaramPrivacy-Preserving ML Efficient Neural Network Inference
University of Southern California, Los Angeles, California, USA
Batch effects can impair federated learning in multi-center omics studies
Dec 8, 2024Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5Batch Effect Correction Secure Multi-Party Computation
Institute for Computational Systems Biomedicine, University of Hamburg, 22761 Hamburg, Germany · Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany · Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark +3

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OptiPrime: Optimizing Private Inference through Protocol-Hardware Co-design

Jiangrui Yu, Ye Yu, Si Chen, Chenqi Lin+4

Peking University, Beijing, China · Open Security Research, Shenzhen, China · Tsinghua University, Beijing, China

Efficient Neural Network Inference Secure Multi-Party Computation AI Accelerator Inference Homomorphic Encryption Privacy-Preserving ML

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Private deep neural network (DNN) inference based on hybrid homomorphic encryption (HE) and multi-party computation (MPC) can protect user data with a formal guarantee, but at the cost of significant latency overhead due to HE. Customized HE accelerators have been proposed and have achieved orders-of-magnitude speedup for individual HE operations. However, when directly applying a commercial HE accelerator to state-of-the-art HE-MPC frameworks, we observe only limited end-to-end performance gain. This is because HE-MPC frameworks often require wireless transmission of input and output ciphertexts for each HE operation, leading to a severe network communication bottleneck. To overcome this challenge, we introduce OptiPrime, a protocol-hardware co-optimization framework for efficient private DNN inference. OptiPrime features a novel HE protocol for convolutions that substantially reduces the number of transmitted output ciphertexts and mitigates the network communication bottleneck. Meanwhile, as the new protocol introduces complex computation for fewer output ciphertext, we observe new memory access challenges due to a high volume of weight plaintexts and intermediate ciphertexts. Hence, we further propose a lightweight compression system for the weight plaintexts, reducing memory traffic by 10 times, as well as a specialized dataflow to maximize on-chip data reuse of intermediate ciphertexts. Extensive experiments show that our framework outperforms the Cheetah baseline by at most 5.7 times on CPUs and 4.2 times with an accelerator.