Research Project Title:
Privacy Preserving Machine Learning Framework
abstract:In today’s world of extensive data-collection, privacy of users is ever more important. Even the so called “anonymized” datasets can be de-anonymized given external records. The notion of differential privacy makes concrete mathematical guarantees and is far more desirable. Through this SuperUROP, I will be working to develop a fully scalable, fault-tolerant, automatically managed Privacy Preserving Machine Learning Framework. Such framework would be indispensable for analysts and developers as it would allow a low-barrier easy to use manage solution for ensuring user privacy while providing high utility at the same time.
Through this SuperUROP, I want to gain more experience in machine learning and privacy research, in addition to making a meaningful contribution to my research group. I’ve taken machine learning, software systems, and algorithms courses and I want to expand on that knowledge with an interesting application. I also hope to publish a paper by the end of SuperUROP for which I aim for significant results.