دانلود کتاب یادگیری ماشینی حفظ حریم خصوصی

Given the popularity of social media and online streaming services, you have probably experienced how far machine learning (ML) can go in delivering personalized services. Even though this exciting field opens doors to many new possibilities and has become an indispensable part of our lives, training ML models requires the use of significant amounts of data collected in various ways. When this data is processed through ML models, it is essential to preserve the confidentiality and privacy of individuals and to maintain the trust of those who use these models. While preserving privacy in ML is essential, it is also challenging. In late 2014, Dr. Chang and his former PhD student, Dr. Mohammad Al-Rubaie, started to investigate the problem of privacy leakage in ML techniques and explored the possibilities of mitigating such leaks. As they continued their research, the Defense Advanced Research Projects Agency (DARPA) initiated a new research program in early 2015 called “Brandeis” (BAA-15-29) with a $50 million initial budget. The program was named after Louis Brandeis, a US Supreme Court Justice who published the “Right to Privacy” paper in the Harvard Law Review journal in 1890. The main goal of the Brandeis program was to seek and develop the technical means to protect private information by enabling safe and predictable sharing of data in which privacy is preserved. Dr. Chang and his team participated in the Brandeis program and developed several privacy-preserving technologies based on compressive privacy and differential privacy techniques.