Call for Papers:
Recent trends have shown a drastic increase in large data repositories by corporations, governments, and healthcare
organizations. These data are collected from various sources, such as crowdsourcing, with or without consent from data
donors. These data create opportunities for developing knowledge and information-based decision making systems by
utilizing data mining. However, there is a significant risk of compromising sensitive information. This risk of
information leakage by using data mining tools has become an obstacle to the advancement of the data science. This
special track solicits papers that discuss various aspects of data privacy from either theoretical or practical perspectives.
Research papers providing real-life privacy solutions, various applications of machine learning, data mining and deep learning
are particularly encouraged.
The IEA/AIE has been a unique platform for computer science research, presenting the latest developments and bringing together
researchers and practitioners. The 31st annual conference is seeking previously unpublished papers offering novel research
contributions in any aspect of the track/conference scopes. Papers may present advances in the theory, implementation, analysis,
or empirical evaluations of software and/or hardware systems. Topics of interest in data science and machine learning include, but
are not limited to, the following:
Important Dates:
Special Track Chairs:
Dr. A. N. K. Zaman, Computer Science, University of Guelph, Canada
Dr. Rozita Dara, Computer Science, University of Guelph, Canada