Abstract
Online networking sites tried their best to have right privacy mechanisms in place for users, enabling them to share the right content with the right audience. With all these efforts, privacy customizations remain hard for users across the sites. Existing research that addresses this problem mainly focuses on semi-supervised strategies that introduce extra complexity by requiring the user to manually specify initial privacy preferences for their friends. In this work, we suggest a deep reinforcement learning framework that can dynamically generate privacy labels for users in OSNs. We evaluated our framework on a 1 year crawl of Twitter data, using different types of recurrent units in recurrent neural networks (RNN): Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple RNN. Our experiments revealed that LSTM performed better than GRU in terms of top users detection accuracy and the ranked dependence between the generated privacy labels and estimated user trust values.
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Notes
- 1.
We extended the framework used in [34] to match the social networks context. The models were built on Keras http://keras.io/, a deep learning library based on Theano.
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Jaradat, S., Dokoohaki, N., Matskin, M., Ferrari, E. (2018). Learning What to Share in Online Social Networks Using Deep Reinforcement Learning. In: Özyer, T., Alhajj, R. (eds) Machine Learning Techniques for Online Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-89932-9_6
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