Due to the emerging privacy-protection laws and regulations (e.g. GDPR in the EU) in recent years, dozens of multi-party computation (MPC for short) protocols have been proposed and widely applied by companies and institutions. These MPC protocols enable companies and institutions to perform joint analyses and machine learning on their private data while protecting their data's privacy. However, due to the complexity of MPC protocols, their implementations of-ten contain data leakage vulnerabilities, which can critically undermine the intended privacy protection. Additionally, most existing security analyses of MPC protocols rely on theoretical proofs, neglecting to detect possible vulnerabilities in MPC im-plementations. Therefore, detecting data leakage vulnerabilities in MPC implementations is an urgent necessity. In this paper, we propose MPCGuard, a practical frame-work for detecting data leakage vulnerabilities in MPC imple-mentations. Different from traditional memory vulnerabilities, data leakage vulnerabilities in MPC implementations cannot be identified by existing sanitizers. To resolve this challenge, we first establish a leakage identifier in MPCGuard with two neural network classifiers to identify whether an MPC implementation contains data leakage vulnerabilities. To enhance identification effectiveness, the structures of neural network classifiers are designed according to the characteristics of MPC protocols. After identifying a data leakage vulnerability, we employ a delta method to assist in locating the vulnerability. To demonstrate the effectiveness of MPCGuard, we apply MPCGuard to test 29 commonly-used MPC implementations in three main-stream MPC frameworks, i.e. Crypten, TF-Encrypted, and MP-SPDZ. We discover that 12 out of 29 implementations contain data leakage vulnerabilities, some of which can lead to the reconstruction of raw data. Until the moment this paper is written, all vulnerabilities, two of which have been assigned with CVE-IDs, have been confirmed. To the best of our knowledge, these two CVE-IDs are the first CVE-IDs assigned for data leakage vulnerabilities in MPC implementations.

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.

Secure multi-party learning (MPL) enables multiple parties to train machine learning models with privacy preservation. MPL frameworks typically follow the peer-to-peer architecture, where each party has the same chance to handle the results. However, the cooperative parties in business scenarios usually have unequal statuses. Thus, Song et al. (CCS’22) presentedpMPL, a hierarchical MPL framework with a privileged party. Nonetheless,pMPLhas two limitations: (i) it has limited configurability requiring manually finding a public matrix that satisfies four constraints, which is difficult when the number of parties increases, and (ii) it is inefficient due to the huge online communication overhead. In this paper, we are motivated to proposeRuyi, a configurable and efficient MPL framework with privileged parties. Firstly, we reduce the public matrix constraints from four to two while ensuring the same privileged guarantees by extending the standard resharing paradigm to vector space secret sharing in order to implement the share conversion protocol and performing all the computations over a prime field rather than a ring. This enhances the configurability so that the Vandermonde matrix can always satisfy the public matrix constraints when given the number of parties, including privileged parties, assistant parties, and assistant parties allowed to drop out. Secondly, we reduce the online communication overhead by adapting the masked evaluation paradigm to vector space secret sharing. Experimental results demonstrate thatRuyiis configurable with multiple parties and outperformspMPLby up to$ 53.87 \times $,$13.91 \times $, and$2.76 \times $for linear regression, logistic regression, and neural networks, respectively.

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, usuallyprivileged party, leads the tasks of machine learning. Only theprivileged party can reveal the final model even if otherassistant 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 ofassistant parties drop out.