Data Privacy and Trustworthy Machine Learning

Trustworthy Networks, Authentication, Privacy, and Security Models

Wireless networks are gaining popularity that comes with the occurrence of several networking technologies raising from personal to wide area, from centralized to distributed, and from infrastructure-based to infrastructure-less. Wireless data link characteristics such as openness of transmission media, makes these networks vulnerable to a novel set of security attacks, despite those that they ...

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Machine Learning and Data

Interactive Anonymization for Privacy aware Machine Learning

Privacy aware Machine Learning is the discipline of applying Machine Learning techniques in such a way as to protect and retain personal identities during the process. This is most easily achieved by first anonymizing a dataset before releasing it for the purpose of data mining or knowledge extraction. Starting in June 2018, this will also remain the sole legally permitted way within the EU to ...

Privacy Preserving Machine Learning: Related Work

A practical scenario of PPML is where only one central party has the entire data on which the ML algorithm has to be learned. Agrawal and Ramakrishnan [1] proposed the first method to learn a Decision Tree classifier on a database without revealing any information about individual records. They consider public model private data setting where the algorithm and its parameters are public whereas ...