Google's TensorFlow gets a new test for training data leaks

Google late last month debuted experimental tests for its TensorFlow Privacy library designed to reduce the degree to which machine learning models leak identifiable personal information in training data sets, such as for biometric facial recognition. The test module enables developers to "assess the privacy properties of their classification models," according to Google. The testing tool is known as a membership inference attack. Obvious applications for the technique include facial recognition and health care. This amounts to a second try for TensorFlow Privacy, which was introduced last year to address the "emerging topic" of privacy in machine learning, Google said.