Federated learning combined with DP-FedAvg or DP-SGD detects online grooming almost as accurately as non-private federated learning, at a user-level privacy cost around epsilon equals 1.
A Novel Way of Identifying Cyber Predators
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abstract
Recurrent Neural Networks with Long Short-Term Memory cell (LSTM-RNN) have impressive ability in sequence data processing, particularly for language model building and text classification. This research proposes the combination of sentiment analysis, new approach of sentence vectors and LSTM-RNN as a novel way for Sexual Predator Identification (SPI). LSTM-RNN language model is applied to generate sentence vectors which are the last hidden states in the language model. Sentence vectors are fed into another LSTM-RNN classifier, so as to capture suspicious conversations. Hidden state enables to generate vectors for sentences never seen before. Fasttext is used to filter the contents of conversations and generate a sentiment score so as to identify potential predators. The experiment achieves a record-breaking accuracy and precision of 100% with recall of 81.10%, exceeding the top-ranked result in the SPI competition.
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Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
Federated learning combined with DP-FedAvg or DP-SGD detects online grooming almost as accurately as non-private federated learning, at a user-level privacy cost around epsilon equals 1.