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

Accurate and Efficient Privacy-Preserving String Matching

A few ANU colleagues and I have just completed a paper on a suffix-tree-based algorithm for computing the longest common substring of two strings in a privacy-preserving manner. Here’s the abstract: The task of calculating similarities between strings held by different organizations without revealing these strings is an increasingly important problem in areas such as … More Accurate and Efficient Privacy-Preserving String Matching

The Success Formula

The Networks scientist Albert-Laszlo Barabasi’s latest book The Formula: The Science Behind Why People Succeed or Fail is a cracker. His six laws of success summarise more than a decade of research into the science of success, in particular how the social network in which we live and operate, with its many kinds of relationships … More The Success Formula

Linking Integer Records: The Simplest Case of PPRL

Privacy-Preserving Record Linkage (PPRL) is one of those problems that still doesn’t have a solid and widely accepted mathematical definition, perhaps because the problem of Record Linkage itself, especially the kind that doesn’t reduce to supervised learning through an abundance of labelled matches, still doesn’t have a solid mathematical definition despite thousands of papers published … More Linking Integer Records: The Simplest Case of PPRL