Category: Data Integration

Integrating Data for AI : All You Ever Wanted To Know About Metadata Standards, Ontologies, Data Models, and Process Models But are Too Afraid to Ask

August 17, 2026

In the last 10 years or so, there has been a paradigm shift in how we train AI models. In particular, the best practice has shifted from Model-Centric AI, where we endless tweak model architectures and algorithms in pursuit of better performance, to what we now call Data-Centric AI, where we keep the model architecture … More Integrating Data for AI : All You Ever Wanted To Know About Metadata Standards, Ontologies, Data Models, and Process Models But are Too Afraid to Ask

Belief Acquisition as Stochastic Filtering

June 16, 2026

We have substantially revised the factored conditional filtering paper to frame it more generally as a solution for belief acquisition in AI agents. Here’s the revised paper on arXiv. https://arxiv.org/abs/2206.02178v3 Here’s the abstract: This paper studies how belief acquisition can be accomplished using stochastic filtering. First, a theoretical foundation for empirical beliefs is outlined. Then … More Belief Acquisition as Stochastic Filtering

Data Security vs Cyber Security

July 20, 2025

Cyber security and data security are closely related concepts that operate at different levels and provide different safeguards. Cyber security is primarily about controlling access to systems and data through different security protection mechanisms, from the physical network layer all the way to the application layer. These security mechanisms come primarily in the form of … More Data Security vs Cyber Security

What Can Differential Privacy Actually Protect?

July 6, 2024

Differential Privacy (DP) is, by now, the most widely adopted formal model of privacy protection used in industry [L23] and government [ABS22] but my sense is that its “semantics”, especially in the presence of correlated data and in the adversarial interactive setting, is still not broadly understood in the community, especially among practitioners. In the … More What Can Differential Privacy Actually Protect?

How To Deal with Database Reconstruction Attacks

March 9, 2024

I have been thinking about data security issues, in particular database-reconstruction attacks. To quote Wikipedia, a reconstruction attack is any method for partially reconstructing a private database from public aggregate information. The question I am specifically interested in is this: Can an attacker with general interactive query access to a dataset recover a piece of … More How To Deal with Database Reconstruction Attacks

Bayesian Filtering on Structured Environments

June 12, 2022

A few colleagues and I have just completed a new research paper titled Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces. The research took over 3 years and I am really excited about the underlying theory and its possible applications. In particular, the paper shows how we can lift’ Bayesian filtering to … More Bayesian Filtering on Structured Environments

A Note on Large Scale Data Matching and Entity Resolution

April 6, 2022

Data matching and entity resolution is a common first step in data preparation and there is a thousand academic papers written on the subject in the literature. In practice, for large datasets – anything more than a million records will do as a definition of large here because most data-matching algorithms can’t handle that because … More A Note on Large Scale Data Matching and Entity Resolution

Towards Fair and Privacy-Preserving Federated Deep Learning Models

May 24, 2020

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

November 3, 2019

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

October 20, 2019

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