Genomic data is usually stored in a decentralized manner among data providers, who cannot share them publicly due to privacy concerns. A significant technical challenge is to combine machine learning and cryptography techniques to build secure machine learning models over distributed datasets without violating privacy. Therefore, data providers in collaborative machine learning want to maintain the privacy of their genomic data, and the researcher who owns the training model wants to keep the model and training methods confidential. This paper proposes a framework that supports secure collaborative learning tasks without disclosing the participants' genomic data and training model information simultaneously. With the help of a cluster of Intel SGX enclaves, our work performs fast distributed training over these enclaves, and a dedicated enclave is solely used for updating the global model. Also, Secure XGBoost was implemented over these hardware enclaves for fast learning and to enhance the enclaves' security with unique data-oblivious algorithms that eliminate side-channel attacks. From the experimental results, our scheme achieves fast and efficient results in collaborative learning systems without an increase in communication overhead, making it practical for large genomic data.

Now various privacy-preserving techniques have been combined with machine learning to ensure training data security. However, during the training, users were unable to select data and labels adaptively in the server, which made the trained models challenging in meeting user needs. Repeated model training not only wastes server resources, but also reduces query efficiency. In this paper, we combine two privacy-preserving technologies, searchable encryption and homomorphic encryption, and propose a highly flexible machine training framework. Homomorphic encryption technology is used to encrypt data, and symmetric searchable encryption technology is used to generate blind indexes and trapdoors. The server can perform ciphertext data search through blind query trapdoors and indexes, and can use the searched homomorphic ciphertext for model training. This framework significantly improves the flexibility of training and the fit of the model, and also ensures the security of dynamic data management. The server locally uses the header of the query trap and the storage address of the ciphertext model to build an automatically updatable ciphertext model table. On the basis of ensuring the effectiveness of the model, it greatly improves the efficiency of obtaining the model, effectively prevents redundant training when different users send the same request, and reasonably allocates resources in the server.

Genome-wide association studies (GWAS) necessitate genomic information of a large population of an individual to achieve reliable results. The human genome can expose sensitive information and is potentially re-identifiable, which raises privacy and security concerns, making individuals deter from contributing their genomic information for such studies. Therefore, there is a need for secure and efficient analysis in which the data owners can securely allocate both the computation and storage on the untrusted cloud. Previous solutions used either a single key setting or far from being practical. Also, efficiency is a big limitation for real life applications. In this paper, we propose a novel hybrid solution that uses the concept of multi-key homomorphic encryption to encrypt ciphertexts using different public keys. To decrypt all the ciphertexts by using a single secret key, we add the re-encryption property of proxy re-encryption to multi-key homomorphic encryption. Also, our framework uses the recently introduced hardware-based architecture (i.e software guard extensions) to securely perform GWAS on genomic data in a privacy-preserving manner and ensure high efficiency by speeding up the computation while preserving the privacy of the data owners. To the best of our knowledge, our scheme is the first multi-key homomorphism combined with intel SGX for analyzing genomic data.