Understanding the relationship between genotypes and phenotypes is crucial for advancing personalized medicine. Expression quantitative trait loci (eQTL) mapping plays a significant role by correlating genetic variants to gene expression levels. Despite the progress made by large-scale projects, eQTL mapping still faces challenges in statistical power and privacy concerns. Multi-site studies can increase sample sizes but are hindered by privacy issues. We present privateQTL, a novel framework leveraging secure multi-party computation for secure and federated eQTL mapping. When tested in a real-world scenario with data from different studies, privateQTL outperformed meta-analysis by accurately correcting for covariates and batch effect and retaining higher accuracy and precision for both eGene-eVariant mapping and effect size estimation. In addition, privateQTL is modular and scalable, making it adaptable for other molecular phenotypes and large-scale studies. Our results indicate that privateQTL is a practical solution for privacy-preserving collaborative eQTL mapping.