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人工智能国际顶会 IJCAI 2017 Workshop征稿
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IJCAI 2017 Workshop: Abuse Preventive Data Mining 2017 URL: https://www.apdm2017.conferences.academy Submission System: https://easychair.org/conferences/?conf=apdm2017 Workshop paper will be invited for extension for the special issue of an international journal of Intelligent Data Analysis (SCI) (https://www.iospress.nl/journal/intelligent-data-analysis/) ========================================================================================== -----~*******~-------- Important Dates -----~*******~-------- Paper Submission Due: 07/05/2017 Author Notification Due: 07/06/2017 Conference Days: 19-21/08/2017 -----~*******~-------- APDM 2017 Workshop Scope -----~*******~-------- This workshop is with the Twenty-sixth International Joint Conference on Artificial Intelligence (IJCAI-17) will be held in Melbourne, Australia, August 19-25, 2017. We solicit contributions on the advanced techniques for Abuse Preventive Data Mining. Data mining critically relies on the information (data and domain knowledge) disclosure from the data curator, and the information accessibility from the data miners. This fully or partial transfer of information ownership, if not done properly, may lead to information abuse. Here, information abuse is referred to the disclosure of important information for which the data owners are not willing to disclose, including but not limited to the private information of users, the trade secret of businesses, etc. Abuse Preventive Data Mining (APDM) aims to curb the potential information abuse across different steps of data mining. There are various related studies such as privacy-preserving data mining, data security, data propriety maintenance, distributed learning, etc. These efforts, however, are disparate in different domains. Now it is the time to revisit from a unified perspective, especially when considering the fact that most related studies can be categorized based on their levels of information ownership transferring: - Full Access to Data: data will be processed before releasing. - Partial Access to Data: distributed data access. - No Access to Data: access to intermediate result. Advances in abuse protective data mining will result in safer collaboration and trusted information sharing. We believe that it is a good time to cover these topics in the workshop, which offers a timely forum for researchers and industry partners to present and discuss latest advances in abuse preventive data mining. |
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