International Association
for Cryptologic Research

High-throughput technologies (e.g., the microarray) have fostered the rapid growth of gene expression data collection. These biomedical datasets, increasingly distributed among research institutes and hospitals, fuel various machine learning applications such as anomaly detection, prediction or clustering. In particular, unsupervised classification techniques based on biclustering like the Cheng and Church Algorithm (CCA) have proven to adapt particularly well to gene expression data. However, biomedical data is highly sensitive, hence its sharing across multiple entities introduces privacy and security concerns, with an ever-present threat of accidental disclosure or leakage of private patient information. To address such threat, this work introduces a novel, highly efficient privacy-preserving protocol based on secure multiparty computation (MPC) between two servers to compute CCA. Our protocol performs operations relying on an additive secret sharing and function secret sharing, leading us to reformulate the steps of the CCA into MPC-friendly equivalents. Leveraging lightweight cryptographic primitives, our new technique named FunBic-CCA is first to exploit the efficiency of function secret sharing to achieve fast evaluation of the CCA biclustering algorithm.