Efficient Privacy-preserving Logistic Model With Malicious Security

Conducting secure computations to protect against malicious adversaries is an emerging field of research. Current models designed for malicious security typically necessitate the involvement of two or more servers in an honest-majority setting. Among privacy-preserving data mining techniques, significant attention has been focused on the classification problem. Logistic regression emerges as a well-established classification model, renowned for its impressive performance. We introduce a novel matrix encryption method to build a maliciously secure logistic model. Our scheme involves only a single semi-honest server and is resilient to malicious data providers that may deviate arbitrarily from the scheme. The $$ -transformation ensures that our scheme achieves indistinguishability (i.e., no adversary can determine, in polynomial time, which of the plaintexts corresponds to a given ciphertext in a chosen-plaintext attack). Malicious activities of data providers can be detected in the verification stage. A lossy compression method is implemented to minimize communication costs while preserving negligible degradation in accuracy. Experiments illustrate that our scheme is highly efficient to analyze large-scale datasets and achieves accuracy similar to non-private models. The proposed scheme outperforms other maliciously secure frameworks in terms of computation and communication costs.