{"id":691908,"date":"2020-09-15T05:43:19","date_gmt":"2020-09-15T12:43:19","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=691908"},"modified":"2020-10-27T09:06:52","modified_gmt":"2020-10-27T16:06:52","slug":"analyzing-information-leakage-of-updates-to-natural-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/analyzing-information-leakage-of-updates-to-natural-language-models\/","title":{"rendered":"Analyzing Information Leakage of Updates to Natural Language Models"},"content":{"rendered":"

To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models.<\/p>\n

We show that a differential analysis of language model snapshots before and after an update can reveal a surprising amount of detailed information about changes in the training data.<\/p>\n

We propose two new metrics—differential score<\/em> and differential rank<\/em>—for analyzing the leakage due to updates of natural language models. We perform leakage analysis using these metrics across models trained on several different datasets using different methods and configurations.<\/p>\n

We discuss the privacy implications of our findings, propose mitigation strategies and evaluate their effect.<\/p>\n","protected":false},"excerpt":{"rendered":"

To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models. We show that a differential analysis of language model snapshots before and after an update can reveal a surprising amount of detailed information about changes in the training data. We propose two new metrics—differential 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