Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research ....Privacy-preserving cloud data mining-as-a-service. This project aims to explore practical privacy-preserving solutions for cloud data mining-as-a-service based on the Intel Software Guard Extensions (SGX) technology. The research addresses privacy concerns of users when outsourcing data mining needs to the cloud. These concerns have increased as more businesses evaluate data mining-as-an outsourced service due to lack of expertise or computation resources. The expected outcomes from the research will include new data privacy models, new privacy-preserving data mining algorithms, and a prototype of cloud data mining software. These will help businesses cut costs for data mining and privacy protection, and provide significant benefits toward helping Australia achieve its national cyber security strategy and potentially provide economic impact from commercialisation of new software technology for the industry partner.Read moreRead less
Constraint-based Privacy Preserving BioSignal Data Management on Blockchain. This project aims to address the issue of user privacy in Bio-Signal data analysis by utilizing the capabilities of differential privacy, smart contracts and blockchain technologies. This project expects to generate new knowledge in the area of privacy to develop an advanced privacy-preserving Bio-Signal data analytic framework. The expected outcomes of this project include increased privacy of user data, and the unifi ....Constraint-based Privacy Preserving BioSignal Data Management on Blockchain. This project aims to address the issue of user privacy in Bio-Signal data analysis by utilizing the capabilities of differential privacy, smart contracts and blockchain technologies. This project expects to generate new knowledge in the area of privacy to develop an advanced privacy-preserving Bio-Signal data analytic framework. The expected outcomes of this project include increased privacy of user data, and the unification of standards on human-specific data analysis, saving time and money spent on privacy breaches. This should provide significant benefits in preserving the quality and integrity of the healthcare services provided by the Australian government and private sector.Read moreRead less
Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false infor ....Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false information as well as recommendation of truthful news to counteract adversarial fake news. This project should generate technologies that enhance the integrity of the online echo system and benefit media providers and online population within Australia and across the world. Read moreRead less
Effective and Efficient Situation Awareness in Big Social Media Data . Crisis management services using traditional methods like phone calls can be easily delayed due to limited communication ability in the disaster area. This project aims to help users make smart decision in critical situations by using big social media data to detect complex social events, receive recommendations, and observe event summaries. We will invent advanced social data models, efficient indices and query techniques fo ....Effective and Efficient Situation Awareness in Big Social Media Data . Crisis management services using traditional methods like phone calls can be easily delayed due to limited communication ability in the disaster area. This project aims to help users make smart decision in critical situations by using big social media data to detect complex social events, receive recommendations, and observe event summaries. We will invent advanced social data models, efficient indices and query techniques for situation awareness in big media. We expect to develop a system to evaluate the proposed situation awareness framework. The outcomes of the project will benefit social media analysis and big data fields. It will also improve the government services by enabling the real time situation awareness in crisis.Read moreRead less
Increasing data quality with group associations in outsourcing environments. Outsourcing of data storage is increasingly common, but poses major problems for data utility and confidentiality. This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments. The project will create a framework that satisfies information protection goals while achieving utility for queries. The developed algorithms and techniques will ....Increasing data quality with group associations in outsourcing environments. Outsourcing of data storage is increasingly common, but poses major problems for data utility and confidentiality. This project aims to discover how tuples (data structures) in fragments can be grouped to increase the utility of queries executed over fragments. The project will create a framework that satisfies information protection goals while achieving utility for queries. The developed algorithms and techniques will formally specify and develop a model to validate loose association rules while minimising data leakage risks. The outcomes will benefit Australians through enabling sharing and linking increasingly large datasets securely and cheaply.Read moreRead less
Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
Sensor stream pattern mining for automatic anomaly recognition and intervention. This project will develop a general framework of accurate automatic recognition of meaningful anomalies in multivariate sensor data streams that require action to avoid detrimental events and allow automatic intervention for efficient mitigation. Existing anomaly recognition algorithms miss many patterns and manually relating co-occurring stream patterns to an anomaly is inefficient and error-prone. The project expe ....Sensor stream pattern mining for automatic anomaly recognition and intervention. This project will develop a general framework of accurate automatic recognition of meaningful anomalies in multivariate sensor data streams that require action to avoid detrimental events and allow automatic intervention for efficient mitigation. Existing anomaly recognition algorithms miss many patterns and manually relating co-occurring stream patterns to an anomaly is inefficient and error-prone. The project expects to develop methods for intercepting a combination of co-occurring patterns to ascertain what an anomaly is and identify the anomaly and its stages that indicate the necessity of intervention. This project will advance techniques for sensor stream data mining and enable general applications of sensor surveillance and automatic mechanical intervention.Read moreRead less
Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. ....Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. Evaluation will be via development of several exemplar applications. The techniques and framework will be applicable to a broad range of economically important problems in areas as diverse as health, travel, scientific publication search, product marketing and software engineering.Read moreRead less
Identifying and Tracking Influential Events in Large Social Networks. This project aims to invent a novel model and techniques for identifying and tracking influential events in large and dynamic social networks in real time. The proposed model would take into account the structure and content of social networks, and the influence of events. The project also plans to develop efficient strategies for identifying and tracking events in large and dynamic social network environments based on the mod ....Identifying and Tracking Influential Events in Large Social Networks. This project aims to invent a novel model and techniques for identifying and tracking influential events in large and dynamic social networks in real time. The proposed model would take into account the structure and content of social networks, and the influence of events. The project also plans to develop efficient strategies for identifying and tracking events in large and dynamic social network environments based on the model, In particular, the project plans to investigate flexible social network query methods to make users’ event search easy. Finally the project plans to build an evaluation system to demonstrate the efficiency of the algorithms and effectiveness of the model.Read moreRead less
Continuous intent tracking for virtual assistance using big contextual data. Recently launched Virtual Assistant products such as Amazon Echo and Google Home are commanded by voice and can call apps to do simple tasks like setting timers and playing music. The next-generation virtual assistants will recommend things to be done proactively rather than waiting for commands passively. This project aims to develop algorithms that can predict what a user intends to do and therefore help virtual assis ....Continuous intent tracking for virtual assistance using big contextual data. Recently launched Virtual Assistant products such as Amazon Echo and Google Home are commanded by voice and can call apps to do simple tasks like setting timers and playing music. The next-generation virtual assistants will recommend things to be done proactively rather than waiting for commands passively. This project aims to develop algorithms that can predict what a user intends to do and therefore help virtual assistants make recommendations that suit users’ needs accurately. It will benefit many service industry sectors of Australia by enabling virtual assistants to provide services proactively.Read moreRead less