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dc.contributor.authorJi, S
dc.contributor.authorLong, G
dc.contributor.authorPan, S
dc.contributor.authorZhu, T
dc.contributor.authorJiang, J
dc.contributor.authorWang, S
dc.date.accessioned2019-06-19T13:09:55Z
dc.date.available2019-06-19T13:09:55Z
dc.date.issued2019
dc.identifier.isbn9783030185893
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-18590-9_17
dc.identifier.urihttp://hdl.handle.net/10072/385246
dc.description.abstractRecent 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.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofconferencenameDatabase Systems for Advanced Applications 24th International Conference, DASFAA 2019
dc.relation.ispartofconferencetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofdatefrom2019-04-22
dc.relation.ispartofdateto2019-04-25
dc.relation.ispartoflocationChiang Mai, Thailand
dc.relation.ispartofpagefrom225
dc.relation.ispartofpageto229
dc.relation.ispartofvolume11448 LNCS
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleDetecting Suicidal Ideation with Data Protection in Online Communities
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorPan, Shirui


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