Infrastructure

Large scale optimisation of the numbers and locations of meters and monitoring devices, including modelling the multiple objectives of system deployment cost and resolution of data collected, is required for effective system design and operation.

OPTIMA CIs: Assoc. Professor Guido Tack

Associates: Dr Mario Boley

PhD candidate: starting soon

Predict-and-Optimise through Time at Water Recycling Treatment Plants

OPTIMA CIs: Professor Kate Smith-Miles

Associates: Professor James Bailey

PhD candidate: VincentBarbosa Vaz

Multi-objective optimisation of environmental outcomes in urban water systems

This project forms part of a broader research program with The University of Melbourne and Melbourne Water that aims to test advances in real-time control technology (RTC) to control a network of small- and large-scale rainwater and stormwater storage. The aim is to simultaneously deliver water supply, flood protection, and delivery of flow regimes into waterways that match the ecological requirements of important aquatic species, such as the platypus.

OPTIMA CIs: Professor Kate Smith-Miles

Associates: Professor Tim Fletcher, Dr Mathew Burns

PhD Candidate: Shiraz (Yi) Zhen

Waterways long-term ambient WQ monitoring network

This project forms part of a broader research program with The University of Melbourne and Melbourne Water that aims to interrogate and optimise the waterways long-term water quality monitoring network. The aim is to identify key physicochemical water quality parameters that best predict stream conditions to allow for targeting of sampling effort and identification of key catchment characteristics linked to water quality and stream values.

OPTIMA CIs: Professor Kate Smith-Miles

Associates: Professor Tim Fletcher

PhD Candidate: Vaughn Gray

Assessing risk of database reconstruction attacks on aggregate tables and linear systems

The increasingly available personal information online and the rise of computing power mean that the threats from database reconstruction attacks (DRAs) are becoming easier to realise. While the ABS has been using perturbation methods to ensure confidentiality there is a strong need to continue assessing the probability of successful DRAs and improving ABS perturbation methodology to detect and prevent DRAs. The aim of this project is to develop prototype tools that use constrained optimisation methods to quantify the amount of unit record data that can be retrieved from perturbed tables of aggregated statistics. . This research addresses the following research questions:1. How can the methods be extended to address DRA risks from multiple perturbed tables with overlapping coverages?2. How can the methods be extended to assess the change in DRA risks as more tables are produced from the same unit record data?3. Can the methods be applied to aggregates involving a mixture of categorical and continuous variable?4. Are any adjustments required to apply the methods at scale – e.g. when thousands of tables are produced from a single dataset, or when trying to reconstruct millions of records from a regression model with many coefficients?

OPTIMA CIs: Peter Taylor and Kate Smith-Miles

Associates: Joseph Chien

PhD Student: Harry Macarthur