arXiv subjects

Guopeng Lin

Publications and source records attributed to Guopeng Lin.

6 recordsLinked to original sources

arXiv2026

Bifrost: A Much Simpler Secure Two-Party Data Join Protocol for Secure Data Analytics

Secure data join enables two parties with vertically distributed data to securely compute the joined table, allowing the parties to perform downstream Secure multi-party computation-based Data Analytics (SDA), such as training machine learning models, based on the joined table. While Circuit-based Private Set Intersection (CPSI) can be used for secure data join, it introduces redundant dummy rows in the joined table, which results in high overhead in the downstream SDA tasks. iPrivJoin addresses this issue but introduces significant communication overhead in the redundancy removal process, as it relies on the cryptographic primitive OPPRF for data encoding and multiple rounds of oblivious shuffles. In this paper, we propose a much simpler secure data join protocol, Bifrost, which outputs (the secret shares of) a redundancy-free joined table. The highlight of Bifrost lies in its simplicity: it builds upon two conceptually simple building blocks, an ECDH-PSI protocol and a two-party oblivious shuffle protocol. The lightweight protocol design allows Bifrost to avoid the need for OPPRF. We also proposed a simple optimization named \textit{dual mapping} that reduces the rounds of oblivious shuffle needed from two to one. Experiments on datasets of up to 100 GB show that Bifrost achieves $2.54 \sim 22.32\times$ speedup and reduces the communication by $84.15\% \sim 88.97\%$ compared to the SOTA redundancy-free secure data join protocol iPrivJoin. Notably, the communication size of Bifrost is nearly equal to the size of the input data. In the two-step SDA pipeline evaluation (secure join and SDA), the redundancy-free property of Bifrost not only avoids the catastrophic error rate blowup in the downstream tasks caused by the dummy rows in the joined table (as introduced in CPSI), but also shows up to $2.80\times$ speed-up in the SDA process with up to $73.15\%$ communication reduction.

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arXiv2025

IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning

Vertical privacy-preserving machine learning (vPPML) enables multiple parties to train models on their vertically distributed datasets while keeping datasets private. In vPPML, it is critical to perform the secure dataset join, which aligns features corresponding to intersection IDs across datasets and forms a secret-shared and joint training dataset. However, existing methods for this step could be impractical due to: (1) they are insecure when they expose intersection IDs; or (2) they rely on a strong trust assumption requiring a non-colluding auxiliary server; or (3) they are limited to the two-party setting. This paper proposes IDCloak, the first practical secure multi-party dataset join framework for vPPML that keeps IDs private without a non-colluding auxiliary server. IDCloak consists of two protocols: (1) a circuit-based multi-party private set intersection protocol (cmPSI), which obtains secret-shared flags indicating intersection IDs via an optimized communication structure combining OKVS and OPRF; (2) a secure multi-party feature alignment protocol, which obtains the secret-shared and joint dataset using secret-shared flags, via our proposed efficient secure shuffle protocol. Experiments show that: (1) compared to the state-of-the-art secure two-party dataset join framework (iPrivjoin), IDCloak demonstrates higher efficiency in the two-party setting and comparable performance when the party number increases; (2) compared to the state-of-the-art cmPSI protocol under honest majority, our proposed cmPSI protocol provides a stronger security guarantee (dishonest majority) while improving efficiency by up to $7.78\times$ in time and $8.73\times$ in communication sizes; (3) our proposed secure shuffle protocol outperforms the state-of-the-art shuffle protocol by up to $138.34\times$ in time and $132.13\times$ in communication sizes.

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arXiv2025

HawkEye: Statically and Accurately Profiling the Communication Cost of Models in Multi-party Learning

Multi-party computation (MPC) based machine learning, referred to as multi-party learning (MPL), has become an important technology for utilizing data from multiple parties with privacy preservation. In recent years, in order to apply MPL in more practical scenarios, various MPC-friendly models have been proposedto reduce the extraordinary communication overhead of MPL. Within the optimization of MPC-friendly models, a critical element to tackle the challenge is profiling the communication cost of models. However, the current solutions mainly depend on manually establishing the profiles to identify communication bottlenecks of models, often involving burdensome human efforts in a monotonous procedure. In this paper, we propose HawkEye, a static model communication cost profiling framework, which enables model designers to get the accurate communication cost of models in MPL frameworks without dynamically running the secure model training or inference processes on a specific MPL framework. Firstly, to profile the communication cost of models with complex structures, we propose a static communication cost profiling method based on a prefix structure that records the function calling chain during the static analysis. Secondly, HawkEye employs an automatic differentiation library to assist model designers in profiling the communication cost of models in PyTorch. Finally, we compare the static profiling results of HawkEye against the profiling results obtained through dynamically running secure model training and inference processes on five popular MPL frameworks, CryptFlow2, CrypTen, Delphi, Cheetah, and SecretFlow-SEMI2K. The experimental results show that HawkEye can accurately profile the model communication cost without dynamic profiling.

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arXiv2024

Ents: An Efficient Three-party Training Framework for Decision Trees by Communication Optimization

Multi-party training frameworks for decision trees based on secure multi-party computation enable multiple parties to train high-performance models on distributed private data with privacy preservation. The training process essentially involves frequent dataset splitting according to the splitting criterion (e.g. Gini impurity). However, existing multi-party training frameworks for decision trees demonstrate communication inefficiency due to the following issues: (1) They suffer from huge communication overhead in securely splitting a dataset with continuous attributes. (2) They suffer from huge communication overhead due to performing almost all the computations on a large ring to accommodate the secure computations for the splitting criterion. In this paper, we are motivated to present an efficient three-party training framework, namely Ents, for decision trees by communication optimization. For the first issue, we present a series of training protocols based on the secure radix sort protocols to efficiently and securely split a dataset with continuous attributes. For the second issue, we propose an efficient share conversion protocol to convert shares between a small ring and a large ring to reduce the communication overhead incurred by performing almost all the computations on a large ring. Experimental results from eight widely used datasets show that Ents outperforms state-of-the-art frameworks by $5.5\times \sim 9.3\times$ in communication sizes and $3.9\times \sim 5.3\times$ in communication rounds. In terms of training time, Ents yields an improvement of $3.5\times \sim 6.7\times$. To demonstrate its practicality, Ents requires less than three hours to securely train a decision tree on a widely used real-world dataset (Skin Segmentation) with more than 245,000 samples in the WAN setting.

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arXiv2022

pMPL: A Robust Multi-Party Learning Framework with a Privileged Party

In order to perform machine learning among multiple parties while protecting the privacy of raw data, privacy-preserving machine learning based on secure multi-party computation (MPL for short) has been a hot spot in recent. The configuration of MPL usually follows the peer-to-peer architecture, where each party has the same chance to reveal the output result. However, typical business scenarios often follow a hierarchical architecture where a powerful, usually privileged party, leads the tasks of machine learning. Only the privileged party can reveal the final model even if other assistant parties collude with each other. It is even required to avoid the abort of machine learning to ensure the scheduled deadlines and/or save used computing resources when part of assistant parties drop out. Motivated by the above scenarios, we propose pMPL, a robust MPL framework with a privileged part}. pMPL supports three-party training in the semi-honest setting. By setting alternate shares for the privileged party, pMPL is robust to tolerate one of the rest two parties dropping out during the training. With the above settings, we design a series of efficient protocols based on vector space secret sharing for pMPL to bridge the gap between vector space secret sharing and machine learning. Finally, the experimental results show that the performance of pMPL is promising when we compare it with the state-of-the-art MPL frameworks. Especially, in the LAN setting, pMPL is around $16\times$ and $5\times$ faster than TF-encrypted (with ABY3 as the back-end framework) for the linear regression, and logistic regression, respectively. Besides, the accuracy of trained models of linear regression, logistic regression, and BP neural networks can reach around 97%, 99%, and 96% on MNIST dataset respectively.

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arXiv2020

SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation

Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The potential solutions could break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academic researchers and industrial vendors are recently strongly motivated to propose two main-stream folders of solutions mainly based on software constructions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short). The above two technical folders have their advantages and limitations when we evaluate them according to the following five criteria: security, efficiency, data distribution, the accuracy of trained models, and application scenarios. Motivated to demonstrate the research progress and discuss the insights on the future directions, we thoroughly investigate these protocols and frameworks of both MPL and FL. At first, we define the problem of Training machine learning Models over Multiple data sources with Privacy Preservation (TMMPP for short). Then, we compare the recent studies of TMMPP from the aspects of the technical routes, the number of parties supported, data partitioning, threat model, and machine learning models supported, to show their advantages and limitations. Next, we investigate and evaluate five popular FL platforms. Finally, we discuss the potential directions to resolve the problem of TMMPP in the future.

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