A privacy-preserving approach for cloud-based protein fold recognition

The bigger pictureIn the era of cloud-based machine learning, privacy concerns, especially in medicine, are critical. Protecting the privacy of medical data is essential for maintaining patient trust and complying with regulations. Recognizing protein folds is vital for understanding diseases and developing treatments, but it currently lacks a privacy-preserving solution. We present an approach that secures this process, allowing the use of advanced models without compromising data or model privacy. By maintaining high performance while ensuring privacy, our scalable and efficient solution demonstrates the practicality of secure cloud-based machine learning in healthcare. This work highlights the urgent need for privacy-conscious cloud-based machine learning and aims to inspire further advancements, emphasizing the importance of data privacy in medical applications.Highlights•We propose private machine learning as a service for protein fold recognition•We combine recurrent kernel networks and multi-party computation•Our approach computes the same result as plaintext RKN without compromising privacy•We show its linear scalability to the parameters of recurrent kernel networksSummaryThe complexity and cost of training machine learning models have made cloud-based machine learning as a service (MLaaS) attractive for businesses and researchers. MLaaS eliminates the need for in-house expertise by providing pre-built models and infrastructure. However, it raises data privacy and model security concerns, especially in medical fields like protein fold recognition. We propose a secure three-party computation-based MLaaS solution for privacy-preserving protein fold recognition, protecting both sequence and model privacy. Our efficient private building blocks enable complex operations privately, including addition, multiplication, multiplexer with a different methodology, most-significant bit, modulus conversion, and exact exponential operations. We demonstrate our privacy-preserving recurrent kernel network (RKN) solution, showing that it matches the performance of non-private models. Our scalability analysis indicates linear scalability with RKN parameters, making it viable for real-world deployment. This solution holds promise for converting other medical domain machine learning algorithms to privacy-preserving MLaaS using our building blocks.