2026· IEEE Transactions on Information Forensics and Security· Vol 21, pp. 7590-7603· 0 citations· 47 references
Computer Science
Abstract
As a classical type of machine learning algorithms, tree models have been widely employed in various fields, such as financial analysis and health diagnostics, offering high-accuracy and low-latency prediction services to users. However, tree evaluation also raises significant privacy concerns, particularly with respect to the tree model and the query sample, while the existing private decision tree evaluation schemes are unable to reach a good trade-off between privacy and efficiency in practice. Therefore, in this paper, we propose an efficient and privacy-preserving tree evaluation scheme based on additive homomorphic encryption, namely PACT. Specifically, PACT introduces an innovative algorithm by leveraging the overflow characteristic of two’s complement to support AHE-based parallel comparison, and it utilizes the lightweight homomorphic addition to select tree paths non-interactively. Meanwhile, we carefully design perturbation and shuffle methods to enhance model and sample privacy. The security of PACT is verified based on the ideal-real paradigm. Experimental results on real-world and synthetic datasets demonstrate the lossless accuracy and superior running efficiency of PACT.
Federated learning is a technology that is used to protect data privacy in machine learning. Nonetheless, in federated learning, updating the global model requires the use of gradient descent algorithm, which involves multiple rounds of interaction between entities to complete the iterative updates, inevitably incurring massive computational and communication overhead. In 2020, Wang et al. first proposed a non-interactive federated regression scheme, which effectively improves the training efficiency of regression models while protecting the privacy of local training data. However, like most current federated regressions, it involves a third authority (TA) to generate keys for each entity, which poses a significant privacy risk and results in considerable communication overhead. From the view of security and practicality, this paper first proposes a multi-party homomorphic encryption algorithm named MPaillier. Furthermore, we have designed PNFR, a privacy-preserving federated learning scheme for regressions training built on the MPaillier algorithm. The participating entities of PNFR are the data owners and a cloud server, eliminating the need for a TA, thus enhancing the practicality and efficiency of the scheme. Experimental results demonstrate that our scheme is $\sim 10^{3}$ times faster than interactive federated regressions PrivFL and about 80% faster than non-interactive federated regressions VANE.
Decision tree classification serves as a fundamental component in many machine learning applications. As inference services are increasingly outsourced to cloud platforms, designing privacy-preserving mechanisms has become important. To secure outsourced decision tree inference, Chen et al. proposed SecDT, an efficient secret-sharing-based framework. While SecDT offers notable efficiency and preliminary security guarantees, we identify a critical vulnerability: attribute leakage. Specifically, the attributes associated with decision tree nodes are exposed to non-owner parties, which risks revealing sensitive model information. In this paper, we first present two secure enhancements, SecDT+v1 and SecDT+v2. These variants use the transformation matrix technique to obfuscate node attributes within the secret-shared domain. To prevent more advanced leakage through attribute access patterns, we design two advanced variants, SecDT+vH and SecDT+vDP, which incorporate dot-product operations to achieve the attribute-hiding property. Extensive evaluations on real-world datasets demonstrate that our proposed schemes provide robust security guarantees while outperforming state-of-the-art solutions in both classification latency and bandwidth efficiency.
Chang-Ching Hung, Tsung-Che Hsieh, Yu-Chi Chen et al.· IEEE Transactions on Informa...· 0 citations
This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination, and identifies promising directions for future research.
Shivendra Shukla, C. S. Gautam, Divyansh Tiwari· International Journal of Cre...· 0 citations
The increasing needs in data sharing in the fields of finance, governance, and artificial intelligence pose a major threat to privacy, particularly in quantum computing. In this paper, a hybrid privacy-preserving system incorporating simulated BB84 Quantum Key Distribution (QKD) to generate secure keys, reversible pseudonymization with encrypted mapping vaults, automatic key rotation, and re-identification risk analysis by machine learning are introduced. Also, optional differential privacy layer allows irreversible anonymization in the cases of analysis. The proposed system will enable two modes, that is, recovery of secure data and the ability to publish data in privacy modes. The experimental findings indicate that authorized users have 100% recovery accuracy, re-identification risk is low and is close to random guessing and data utility is acceptable given the privacy restrictions. FastAPI and Streamlit are used to implement the framework, which is appropriate in the real-world deployment in clouds.
Srividhya Ganesan, G. Vijayasekaran, S. R.· 2026 4th International Confe...· 0 citations
Federated learning (FL) enables collaborative model training across multiple clients in a privacy-preserving manner. However, the employment of homomorphic encryption algorithms might lead to high computational cost while the application of differential privacy (DP) methods would sacrifice model performance. To establish efficient and secure FL system as well as maintaining competitive performance, we introduce a DP-enabled cascaded filter with novel model-aggregation mechanism. Taking the model parameters of one client for example, dimensions with large absolute values and significant variations are selected by the cascaded filter and regarded as important dimensions. After that, random noise are added to these important dimensions for data security. Finally, the model parameters will be sent to the central server for aggregation. Unlike traditional DP-based approaches, our method considers each dimension’s informational importance, i.e., both absolute value and its variation. We theoretically prove the convergence of our method and verify the effectiveness on different scenarios including four datasets. The experimental results suggest that our method outperforms the other ones from literature under both IID and non-IID conditions while achieving high level of efficiency and privacy protection performance.
Zhiqiang Chen, Yuchen Jiang, Ray Y. Zhong et al.· IEEE Transactions on Informa...· 0 citations
This work proposes a practical non-interactive encrypted retrieval framework for RAG based on threshold selection, and introduces a precision-stable mask polarization method that ensures accurate recovery of selected documents.
Yang Gao, Gang Quan, Scott Piersall et al.· arXiv.org· 0 citations
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