The rampage of incessant cyber attacks have caused the disclosure of billions of users’ private data, shaking the Internet to its core. In response, various data privacy laws and regulations have emerged, forcing the industry to change their practice and bringing the demand for large-scale secure computing to the spotlight. Such a demand, however, cannot be met by the state-of-the-art cryptographic techniques, even with decades of effort, due to the overheads (speed, bandwidth consumption) they incur. To narrow the gap, recent years have seen rapid progress in hardware based trusted execution environments (TEE), such as Intel SGX, AMD SEV and ARM TrustZone, which enable efficient computation on encrypted data within a secure enclave established by a trusted processor. In this talk, I will present our research on understanding and addressing the security challenges in this new secure computing paradigm and enhancing its design to achieve scalability, for the purpose of supporting accelerated machine learning. Further I will present the big questions that need to be answered in the area and introduce our genome privacy competition as a synergic activity that helps move the science in this area forward.