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APDM 2017 : IJCAI 2017 Workshop: Abuse Preventive Data Mining 2017

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Link: http://www.apdm2017.conferences.academy
 
When Aug 19, 2017 - Aug 21, 2017
Where Melbourne
Submission Deadline May 7, 2017
Notification Due Jun 7, 2017
Categories    data mining   data privacy   data security   information abuse
 

Call For Papers


*** APDM 2017 Call for Papers ***


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IJCAI 2017 Workshop: Abuse Preventive Data Mining 2017

URL: http://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)

(http://www.iospress.nl/journal/intelligent-data-analysis/)



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Important Dates
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Paper Submission Due: 07/05/2017
Author Notification Due: 07/06/2017
Conference Days: 19-21/08/2017


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APDM 2017 Workshop Scope
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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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Topics of Interest
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To further contribute to the understanding of Abuse Preventive Data Mining, we invite original
articles in relevant topics, which include but are not limited to:

* Formal Methods
- Statistical Framework
- Privacy Utility Tradeoff

* Information Abuse
- Ownership Abuse
- Ownership Transfer
- Abuse Prevention

* Data Analytics
- Trusted Data Flow
- Trusted and Trustworthy Data Mining

* Data Privacy
- Privacy Preserving Intelligent Systems
- Privacy Preserving Data Publishing
- Privacy in Information Sharing
- Economics of Privacy

* Data Security
- Data Permission Abuse
- Electronic Commerce Security
- Data Protection in Outsourcing

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Paper Submissions
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Formatting Guidelines, LaTeX Styles and Word Template can be dowloaded
from http://ijcai-17.org/FormattingGuidelinesIJCAI-17.zip.

The submission site is
available at https://easychair.org/conferences/?conf=apdm2017


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Publication
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Workshop papers accepted and presented in APDM 2017 will be
invited to be extended for the possible inclusion of special issue of
journal of Intelligent Data Analytics.

Please note that authors of accepted special issue articles are required
to pay US$350 or €300 publication fee before their papers being published
by journal of Intelligent Data Analytics.


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Organizers
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- Gang Li, Deakin University, Australia
- Zhi-Hua Zhou, Nanjing University, China


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PC Members
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- Mohammad Alaggan, Helwan University, Egypt
- Ruichuan Chen, Nokia Bell Labs
- Rui Chen, Samsung Research America, USA
- Chris Clifton, Purdue University, USA
- Josep Domingo-Ferrer, Universitat Rovira i Virgili, Spain
- Yuan Hong, University at Albany, USA
- Shiva Kasiviswanathan, Pennsylvania State University, USA
- Xiang-Yang Li, Illinois Institute of Technology, USA
- Ninghui Li, Purdue University, USA
- Xiaofeng Meng, Renmin University of China, China
- Kui Ren, State University of New York at Buffalo, USA
- Yilin Shen, Samsung Research America, USA
- Xintao Wu, University of Arkansas, USA
- Yin Yang, Hamad Bin Khalifa University, Qatar
- Ting Yu, Qatar Computing Research Institute, Qatar
- Philip Yu, University of Illinois at Chicago, USA
- Xiaojian Zhang, Henan University of Economics and Law, China
- Sheng Zhong, Nanjing University, China

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