Privacy-Preserving Machine Learning by J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera.
Morris holds a BSEE from Tatung University, Taiwan and MS and PhD in computer engineering from North Carolina State University. He teaches at the University of South Florida. Di holds PhD in Computer Engineering from Iowa State University and the University of South Florida. He is a Security / Privacy Engineer at Snap Inc. Dumindu holds a MSc in Enterprise Application Development from Sheffield Hallam University and PhD in Electrical Engineering and Philosophy from University of South Florida. Today he is Assistant Professor of Data Science at Embry-Riddle Aeronautical University.
This was a book that places into perspective the need for ensuring privacy in our fast paced AI marketplace. The authors express the need not only to understand privacy within Machine Learning systems, but understanding methodologies to preserve user’s private data while maintaining performance on LLMs.
They address how personal data well embedded across various sectors increases the risks of data breaches. Just realize how your smartphone is tracked by marketing companies. In fact, they review the Facebook-Cambridge Analytica scandal and call for robust privacy measures in data-driven applications.
