Understanding Industry Structure and Competition

While going through some old documents this week, I rediscovered a set of notes I took while reading Michael Porter many years ago. On rereading the notes, I find (again) his ideas to be really simple and compelling, worthy of sharing as a mental model for both practising data scientists and a value investors. A … More Understanding Industry Structure and Competition

Towards Fair and Privacy-Preserving Federated Deep Learning Models

My former postdoc Lingjuan Lyu has been working with a few research collaborators on a fair and privacy-preserving federated deep-learning framework and a paper describing the framework has just been published at the IEEE Transactions on Parallel and Distributed Systems. Here’s the paper details: Title: Towards Fair and Privacy-Preserving Federated Deep Models Abstract: The current … More Towards Fair and Privacy-Preserving Federated Deep Learning Models

Distributed Privacy-Preserving Prediction

Another day, another paper, this time by my postdoc Lingjuan Lyu and a few collaborators. Here’s the abstract: In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a … More Distributed Privacy-Preserving Prediction