Re-use of patients’ health records can provide tremendous benefits for clinical research. One of the first essential steps for many research studies, such as clinical trials or population health studies, is to effectively identify, from electronic health record systems, groups of well-characterized patients who meet specific inclusion and exclusion criteria. This procedure is called cohort exploration. Yet, when researchers need to compile specific cohorts of patients, privacy issues represent one of the major obstacles to accessing patients’ data, especially when sensitive data, such as genomic data, are involved. Because of this, cohort exploration could become extremely difficult and time-consuming. In this joint paper between the Ecole Polytechnique F ´ ed´ erale de Lausanne (EPFL) and the Lausanne University Hospital ´ (CHUV), we address the challenge of designing and deploying an efficient privacy-preserving explorer for genetic cohorts. Our solution is built on top of i2b2 (informatics for integrating biology and the bedside), the state-of-the-art open-source framework for cohort exploration, and exploits on cutting-edge privacy-enhancing technologies (PETs) such as homomorphic encryption and differential privacy. To the best of our knowledge, our proposed solution is the first of its kind to be successfully deployed in a real operational environment within a hospital. Especially, it has been tested as one of the services of the clinical research data-warehouse of CHUV. Solutions involving homomorphic encryption are often believed to be costly and still immature for use in operational environments. In this paper, we prove the opposite by describing how actually, for specific use cases, this kind of PETs can be very efficient enablers.