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 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.
Original language | English |
---|---|
Title of host publication | Database Systems for Advanced Applications |
Subtitle of host publication | DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA Chiang Mai, Thailand, April 22–25, 2019 Proceedings |
Editors | Guoliang Li, Jun Yang, Joao Gama, Juggapong Natwichai, Yongxin Tong |
Place of Publication | Cham Switzerland |
Publisher | Springer |
Pages | 225-229 |
Number of pages | 5 |
ISBN (Electronic) | 9783030185909 |
ISBN (Print) | 9783030185893 |
DOIs | |
Publication status | Published - 2019 |
Event | Database Systems for Advanced Applications 2019 - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019 Conference number: 24th https://dasfaa2019.eng.cmu.ac.th/ https://link.springer.com/book/10.1007/978-3-030-18576-3 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
---|---|
Publisher | Springer |
Volume | 11448 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Database Systems for Advanced Applications 2019 |
---|---|
Abbreviated title | DASFAA 2019 |
Country/Territory | Thailand |
City | Chiang Mai |
Period | 22/04/19 → 25/04/19 |
Internet address |