5月22日晚18:00,《Privacy Protection in Machine Learning》讲座如期举行。本次活动邀请了我校信息管理与工程学院博二研究生杨云骢同学,他的主要的研究方向为隐私计算与数据安全。研究的兴趣点包括机器学习中的隐私与效用的衡量与权题。 讲座伊始,杨云...
Privacy Protection in Machine Learning》 讲座在等你呦! 背景介绍 隐私安全一直以来都是人类社会关注的重点问题。随着信息时代的到来与机器学习技术的飞速发展,人们对于隐私安全的定义越来越广泛,与此同时,我们作为信息时代的参与者与建设者,也正面临...
Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different a...
If you’re interested in learning more about the different ways Microsoft protects your data, please visit the Microsoft Trust Center (opens in new tab). Read more about how Microsoft approaches encryption in the cloud (opens in new tab). Learn about the data protection resources (...
Machine learning has become prevalent in transforming diverse aspects of our daily lives through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular systems, and automated threat detection and triage are some prominent use cas
Is privacy protection even possible? While there have been many proposed methods to reduce memorization in machine learning methods, most have beenlargely ineffective. Currently, the most promising solution to this problem is to ensure a mathematical limit on the privacy risk. ...
Section 2 provides a literature review over privacy-preserving machine learning based on differential privacy protection. Section 3 presents some notations and definitions on cryptographic primitives and differential privacy. In Section 4, we present the system model, the problem statement and the ...
the implementation of technical solutions to simultaneously address the demands for data protection and utilization is mandatory. Here we present an overview of current and next-generation methods for federated, secure and privacy-preserving artificial intelligence with a focus on medical imaging application...
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 (...
Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall, organizations can now use Machine Learning as a Service (MLaaS) engines...