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Privacy Engineering

Governments all around the world have adopted the open data initiative through which information is made usable, reusable and accessible to the public. As the analysis of these datasets provides profound insights into a number of key areas of society (e.g., healthcare, medical and government services), the datasets are often shared or released to the public. There are two key features of such datasets: (a) the data size may be large and come from a variety of sources in a variety of forms (e.g., large-scale high-dimensional, set-valued or transactional datasets); (b) the datasets are released incrementally as and when they become available. The privacy-sensitive information (e.g. information about customers or patients) in such datasets often require strong preservation before the datasets are released. One of the major challenges is thus to maintain privacy in releasing and processing such big incremental open datasets without reducing the utility (e.g. benefits achieved by processing such data). A variety of privacy preserving approaches have been proposed and extensively studied in computer science (specifically data mining and cryptography) and statistics (including statistical disclosure control). However, there is a need to provide methodologies, tools and techniques to support the application of any privacy preserving techniques meeting acceptable levels of privacy (in terms of complying with national guidance and regulations).

DSS has developed different frameworks, tools and techniques that aim to meet the Australian Privacy Principles (APP). Our unique technical capability is to combine the applied cryptography and statistical disclosure techniques within a single framework. Our solutions include:

  • A framework for conducting Privacy Impact Assessment (PIA) against the APP.
  • The risk assessment framework for static one time data and longitudinal data using existing privacy-preservation techniques.
  • Data privacy architecture and corresponding access policies.
  • Note: In consultation with Liming Zhu and Aruna Seniviratne, DSS has significantly reduced its efforts in this area within Data61. In addition, Julian Jang-Jaccard and Xuyun Zhang have left Data61. The personnel hired as replacements have expertise in the new focus area outlined the science vision section earlier.

 

  • Successful completion of the Telehealth trial project leads to a collaborative project funded through the NICTA Big Trust project.
  • Analytics was run successfully as the first Data61 project through the first ON program.
  • Our work in this area is specifically driven by two government funded deliverable projects:
    • Work Stream 5.2 for Energy Use Data Model (in collaboration with the Energy Business Unit). The Energy Use Data Model project in collaboration with the Energy Business Unit has been running for the past two years and has been extended until 2022.
    • Home monitoring of chronic disease for aged care – NBN Telehealth Program (in collaboration with the Health and Bio-Security Business Unit). The Telehealth project was completed in May 2016 at a total investment of ~A$5.4 Mil (file:///C:/Users/ nep001/Downloads/Telehealth-Trial-Final-Report-May-2016_3-Final.pdf).
  • Shaghayegh Sharif, Paul Watson, Javid Taheri, Surya Nepal, Albert Y. Zomaya: Privacy-aware scheduling SaaS in high performance computing environments. IEEE Trans. Parallel Distrib. Syst. 28(4): 1176-1188 (2017) [IF 4.1]
  • Xuyun Zhang, Wan-Chun Dou, Jian Pei, Surya Nepal, Chi Yang, Chang Liu, Jinjun Chen: Proximity-Aware Local-Recoding Anonymization with MapReduce for scalable big data privacy preservation in cloud. IEEE Trans. Computers 64(8): 2293-2307 (2015) [IF 2.9] [Google Scholar Citation: 24]
  • Xuyun Zhang, Chang Liu, Surya Nepal, Suraj Pandey, Jinjun Chen: A privacy leakage upper bound constraint-based approach for cost-effective privacy preserving of intermediate data sets in cloud. IEEE Trans. Parallel Distrib. Syst. 24(6): 1192-1202 (2013) [IF 4.1] [Google Scholar Citation: 113]