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Privacy Preservation for Wireless Sensor Networks in Healthcare: State of the Art, and Open Research Challenges
The advent of miniature biosensors has generated numerous opportunities ...
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Deep Learning Application in Security and Privacy -- Theory and Practice: A Position Paper
Technology is shaping our lives in a multitude of ways. This is fuelled ...
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Small Sample Learning in Big Data Era
As a promising area in artificial intelligence, a new learning paradigm,...
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Image Obfuscation for Privacy-Preserving Machine Learning
Privacy becomes a crucial issue when outsourcing the training of machine...
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"Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics
The obligation to wear masks in times of pandemics reduces the risk of s...
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A Deep Learning Approach for Privacy Preservation in Assisted Living
In the era of Internet of Things (IoT) technologies the potential for pr...
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On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense int...
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When Machine Learning Meets Privacy: A Survey and Outlook
The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning (ML) is still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This paper surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.
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