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UID:a62ae8d3afe7be3b2fbda702bb4ce8c3-424
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DESCRIPTION:How do recommendation systems work? And, what are their privacy implications?
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/424/how-do-recommendation-systems-work-and-what-are-their-privacy-implications/
SUMMARY:Have you ever wondered how good Spotify, Netflix, Amazon, ..  give you such great recommendations for what to listen, watch, purchase, and live our lives? The magic behind their recommendations are the deep learning machine learning models. These models capture seemingly end-less amounts of information about our online behavior and transform these behaviors into embeddings for future recommendations. These machine learning models are massive (think of terabytes of data), trained on equally imposing set of training samples, called sparse features.  Every click, purchase, and even a mouse hover on a website is a sparse feature for training the model. In this talk I will first provide an overview of how current generation recommendation systems work. If these models can recommend so well, then, they must also know a lot about us.  In fact, they do. By simply observing features such as click history and object interactions an attacker can de-anonymize users with extremely high probability, or track users across different interaction sessions. For instance, if you tell me what are the last two items you purchased online, I can track you with 97% accuracy. All of which is to say there is a lot of interesting privacy research that needs to get done.
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