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  • Detecting Suicidal Ideation with Data Protection in Online Communities

    Author(s)
    Ji, S
    Long, G
    Pan, S
    Zhu, T
    Jiang, J
    Wang, S
    Griffith University Author(s)
    Pan, Shirui
    Year published
    2019
    Metadata
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    Abstract
    Recent advances in Artificial Intelligence empower proactive social services that use virtual intelligent agents to automatically detect people’s suicidal ideation. Conventional machine learning methods require a large amount of individual data to be collected from users’ Internet activities, smart phones and wearable healthcare devices, to amass them in a central location. The centralized setting arises significant privacy and data misuse concerns, especially where vulnerable people are concerned. To address this problem, we propose a novel data-protecting solution to learn a model. Instead of asking users to share all their ...
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    Recent advances in Artificial Intelligence empower proactive social services that use virtual intelligent agents to automatically detect people’s suicidal ideation. Conventional machine learning methods require a large amount of individual data to be collected from users’ Internet activities, smart phones and wearable healthcare devices, to amass them in a central location. The centralized setting arises significant privacy and data misuse concerns, especially where vulnerable people are concerned. To address this problem, we propose a novel data-protecting solution to learn a model. Instead of asking users to share all their personal data, our solution is to train a local data-preserving model for each user which only shares their own model’s parameters with the server rather than their personal information. To optimize the model’s learning capability, we have developed a novel updating algorithm, called average difference descent, to aggregate parameters from different client models. An experimental study using real-world online social community datasets has been included to mimic the scenario of private communities for suicide discussion. The results of experiments demonstrate the effectiveness of our technology solution and paves the way for mental health service providers to apply this technology to real applications.
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    Conference Title
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume
    11448 LNCS
    DOI
    https://doi.org/10.1007/978-3-030-18590-9_17
    Subject
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/385246
    Collection
    • Conference outputs

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