4,758 research outputs found
Machine Learning Models that Remember Too Much
Machine learning (ML) is becoming a commodity. Numerous ML frameworks and
services are available to data holders who are not ML experts but want to train
predictive models on their data. It is important that ML models trained on
sensitive inputs (e.g., personal images or documents) not leak too much
information about the training data.
We consider a malicious ML provider who supplies model-training code to the
data holder, does not observe the training, but then obtains white- or
black-box access to the resulting model. In this setting, we design and
implement practical algorithms, some of them very similar to standard ML
techniques such as regularization and data augmentation, that "memorize"
information about the training dataset in the model yet the model is as
accurate and predictive as a conventionally trained model. We then explain how
the adversary can extract memorized information from the model.
We evaluate our techniques on standard ML tasks for image classification
(CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20
Newsgroups and IMDB). In all cases, we show how our algorithms create models
that have high predictive power yet allow accurate extraction of subsets of
their training data
Automatic Detection of Online Jihadist Hate Speech
We have developed a system that automatically detects online jihadist hate
speech with over 80% accuracy, by using techniques from Natural Language
Processing and Machine Learning. The system is trained on a corpus of 45,000
subversive Twitter messages collected from October 2014 to December 2016. We
present a qualitative and quantitative analysis of the jihadist rhetoric in the
corpus, examine the network of Twitter users, outline the technical procedure
used to train the system, and discuss examples of use.Comment: 31 page
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