Data sharing in the age of deep learning How can we protect personal information and the integrity of artificial intelligence models when sharing data?
) from a collaboration between multiple hospitals that predicted response to neoadjuvant chemotherapy in triple-negative breast cancer was published at the beginning of this year. These projects have demonstrated the potential applications of data sharing and using AI models, while respecting privacy and intellectual property.
These attacks on data privacy can be defended against, using large batches of training data that tend to obscure the effect of individual records, differential privacy, secure multiparty computation or homomorphic encryption . Although effective defenses against data leakage are possible, concerns remain that, with ever-increasing computing power, algorithms that are considered secure today might become breakable in the future and data could be reconstructed from retrospective datasets.
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