A university-based clinical exercise facility that serves as a clinical, teaching and research space

Deakin University opened its Clinical Exercise Learning Centre (CELC) in May 2011, initially staffed by four (now seven) Accredited Exercise Physiologists (AEP), and funded by the university. The main objectives of CELC are to provide (i) excellent clinical practicum learning opportunities for postgraduate students enrolled in the Master of Clinical Exercise Physiology that prepare students for subsequent external placements; (ii) learning opportunities that are vertically integrated with the preparatory components of the Masters, including pathophysiology units and pre-clinical units; (iii) learning opportunities that are also integrated with the external clinical practicum program that is embedded in the Masters; (iv) a clinical service to the community and strong referral networks with local GPs; (v) a research centre that is focussed on evaluating the efficacy of Accredited Exercise Physiology (AEP) services for a range of clinical situations, with a view to contributing to a future national evidence-based practice network supported by ESSA. Deakin University funds the CELC facility, equipment, consumables, limited car parking, practice management software and server and, most importantly, the staff. Therefore CELC runs at a loss even against fees charged and this was built into the original model. Staff include an AEP clinical practicum coordinator, two casual AEPs and several academic AEPs; the latter practise as a small part of their approved workloads. The practice model is for all AEPs to provide clinical services with referred clients who are billed as if CELC is a private practice, whilst concurrently teaching and mentoring students; the latter are expected to be active learners in CELC and have exposure to a wide range of pathologies and clinical situations. Billable hours are always provided by AEPs, not students, but students can assist. CELC provides clinical services 1:1:1 (client: AEP: student), 1:1:5 and 8:1:5. CELC was awarded national runner-up in the ESSA Exercise Physiology clinic of the year in 2011 and has grown its caseload to > 200 referrers in 2013. CELC recently designed a generic research platform and has begun to roll out research projects that are designed to translate 'traditional' research-based evidence of exercise benefits for chronic disease in order to evaluate AEP efficacy of practice in the Australian context. CELC provides a model for other universities, provided those universities see it for its learning value, and not to generate revenue or profit.

A differentially private algorithm for location data release

The rise of mobile technologies in recent years has led to large volumes of location information, which are valuable resources for knowledge discovery such as travel patterns mining and traffic analysis. However, location dataset has been confronted with serious privacy concerns because adversaries may re-identify a user and his/her sensitivity information from these datasets with only a little background knowledge. Recently, several privacy-preserving techniques have been proposed to address the problem, but most of them lack a strict privacy notion and can hardly resist the number of possible attacks. This paper proposes a private release algorithm to randomize location dataset in a strict privacy notion, differential privacy, with the goal of preserving users’ identities and sensitive information. The algorithm aims to mask the exact locations of each user as well as the frequency that the user visits the locations with a given privacy budget. It includes three privacy-preserving operations: private location clustering shrinks the randomized domain and cluster weight perturbation hides the weights of locations, while private location selection hides the exact locations of a user. Theoretical analysis on privacy and utility confirms an improved trade-off between privacy and utility of released location data. Extensive experiments have been carried out on four real-world datasets, GeoLife, Flickr, Div400 and Instagram. The experimental results further suggest that this private release algorithm can successfully retain the utility of the datasets while preserving users’ privacy.

Differentially private random forest with high utility

Privacy-preserving data mining has become an active focus of the research community in the domains where data are sensitive and personal in nature. For example, highly sensitive digital repositories of medical or financial records offer enormous values for risk prediction and decision making. However, prediction models derived from such repositories should maintain strict privacy of individuals. We propose a novel random forest algorithm under the framework of differential privacy. Unlike previous works that strictly follow differential privacy and keep the complete data distribution approximately invariant to change in one data instance, we only keep the necessary statistics (e.g. variance of the estimate) invariant. This relaxation results in significantly higher utility. To realize our approach, we propose a novel differentially private decision tree induction algorithm and use them to create an ensemble of decision trees. We also propose feasible adversary models to infer about the attribute and class label of unknown data in presence of the knowledge of all other data. Under these adversary models, we derive bounds on the maximum number of trees that are allowed in the ensemble while maintaining privacy. We focus on binary classification problem and demonstrate our approach on four real-world datasets. Compared to the existing privacy preserving approaches we achieve significantly higher utility.

Differentially private multi-task learning

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.