{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/120914"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/120914","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Practical Privacy-Preserving Federated Learning with Secure Multi-Party Computation","abstract":"In a world with ever greater need for machine learning and artificial intelligence, it has be- come increasingly important to offload computation intensive tasks to companies with the compute resources to perform training on potentially sensitive data. In applications such as finance or healthcare, the data providers may have a need to train large quantities of data, but cannot reveal the data to outside parties for legal or other reasons. Originally, using a decentralized training method known as Federated Learning (FL) was proposed to ensure data did not leave the client's device. This method still was susceptible to attacks and further security was needed. Multi-Party Computation (MPC) was proposed in conjunction with FL as it provides a way to securely compute with no leakage of data values. This was utilized in a framework called SAFEFL, however, it was extremely slow. Reducing the computation overhead using programming tools at our disposal for this frame- work turns it from a unpractical to useful design. The design can now be used in industry with some overhead compared to non-MPC computing, however, it has been greatly im- proved.","abstract_html":"In a world with ever greater need for machine learning and artificial intelligence, it has be- come increasingly important to offload computation intensive tasks to companies with the compute resources to perform training on potentially sensitive data. In applications such as finance or healthcare, the data providers may have a need to train large quantities of data, but cannot reveal the data to outside parties for legal or other reasons. Originally, using a decentralized training method known as Federated Learning (FL) was proposed to ensure data did not leave the client's device. This method still was susceptible to attacks and further security was needed. Multi-Party Computation (MPC) was proposed in conjunction with FL as it provides a way to securely compute with no leakage of data values. This was utilized in a framework called SAFEFL, however, it was extremely slow. Reducing the computation overhead using programming tools at our disposal for this frame- work turns it from a unpractical to useful design. 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