With colleagues Teague and Culnane, I helped uncover one of the largest privacy breaches in Australian history 2016–17. Federal health and human services in mid-2016 released an open dataset of 30 years of Medicare and Pharmaceutical Benefits Schemes transaction records, for 10% of the Australian population. The intention was to drive health economics research, for evidence-based policy development. Unfortunately minimal privacy protections were in place, while the data reported sensitive treatments e.g., for AIDS, late-term abortions, etc. Initially we completely reidentified doctors, due to improper hashing of their IDs. As a result the dataset was taken offline and a public statement released by the Department. It could not be recalled. A year later we announced we had reidentified patients such as well-known figures in Australian sport and politics.
The day after Medicare’s retraction, the Attorney General published a plan to legislate against reidentification of Commonwealth datasets. In the months to come the Reidentification Criminal Offence Bill (an amendment to the Privacy Act 1988) was introduced to Parliament criminalising the act of reidentification, unless with prior permission. The bill, if passed, would be retroactively applied and reverse the burden of proof on accused. While stifling security experts and journalists responsibly disclosing existing privacy breaches to the government, the bill would not prevent private corporations or foreign entities outside Australian jurisdiction from misusing Commonwealth data. Of 15 submissions to the ensuing Parliamentary Inquiry examining the appropriateness of the bill, 14 were against including the Law Council of Australia, Australian Bankers’ Association, and EFF. Our submission to the inquiry achieved significant impact, being directly quoted 9 times in the Senate Committee’s final report. We wrote an Op-Ed in the Sydney Morning Herald clearly explaining why criminalising reidentification would do more harm than good.
Colleagues Culnane, Teague and I demonstrated a significant reidentification of public transport users, in a 2018 datathon dataset of fine-grained touch-on touch-off events on Victorian public transport (the Myki card). We were able to find ourselves, triangulate a colleague based on a single cotravelling event, and a State MP through linking with social media postings. The privacy breach was significant: it would be easy for stalkers to learn when children travel alone, where survivors of domestic abuse now live, etc. from the release. The Victorian privacy commissioner OVIC’s investigation found PTV had broken state laws.
With colleagues Ohrimenko, Culnane, Teague, I have contributed towards several technical privacy assessments of government data initiatives and privacy sector projects. Contracted by the Australian Bureau of Statistics (ABS), we have for example analysed the privacy of several options for name encoding for private record linkage—as might be used for Australian Census data for example. For Transport for NSW, we have performed a technical privacy assessment of a Data61-processed dataset of Opal transport card bus, train, ferry touch ons/offs again under contract. The data has subsequently been published. We have also discovered vulnerabilities in the hashing methodology published by the UK Office of National Statistics in a third privacy assessment (explained here). These are non-exhaustive examples of technical assessments performed by the group. Common themes to this work are reflected in our 2018 report for the Office of the Victorian Information Commissioner. These and broader issues around robustness of AI are summarised in an OVIC book chapter.
In 2020, the COVID-19 pandemic swept across the world with devistating consequences. An important strategy for slowing the spread of the coronavirus is contact tracing, which traditionally had been manual, laborious but accurate. Naturally many governments hoped that high uptake of Bluetooth-enabled smart phones could be leveraged for automated contact tracing, supporting over-burdened human contact traces if and when COVID-19 put strain on health systems. In March Australia opted to adopt and adapt Singapore’s Bluetooth contact tracing system TraceTogether in its app called COVIDSafe. While the contact tracing effectiveness of the system has since come in to focus, many tech commentators and researchers identified seemingly unnecessary compromise in the system’s privacy provisions. I reported of these privacy critiques with colleagues Farokhi (Melbourne), Asghar and Kaafar (Macquarie), a report that was cited by the government’s COVIDSafe PIA. Like many other experts, we aimed to highlight the flexibility and greater privacy of decentralised approaches. With colleagues Leins and Culnane (Melbourne), we wrote in the MJA on a broader range of issues on the techno-legal and ethical dilemma’s for automated contact tracing. For more up to date detail on the COVIDSafe implementation interested readers should check out the thorough posts by Vanessa et al..
Through 2016 my group with colleague Bailey collaborated with the Austin Hospital’s transplantation unit, on predicting outcomes (graft failure) of liver transplantation for Australian demographics. With machine learning-based approaches, PhD student Yamuna Kankanige could improve by over 20% the predictive accuracy of the Donor Risk Index [Transplantation’17]—a risk score widely used by Australian surgeons today, in planning transplants and follow-up interventions.
In 2011 with Narayanan (now Princeton) and Shi (now Cornell), I helped demonstrate the power of privacy attacks to Kaggle (a $16m Series A, Google acquired platform for crowdsourcing machine learning) [IJCNN’11]. After determining the source of an anonymised social network dataset, intended for use in a link prediction contest, we downloaded and linked it to the competition test set. Normally a linkage attack would end there, having re-identified users. We used it to look up correct test answers and win the competition by ‘cheating’. No privacy breach resulted and contestants remained able to compete. However the result raised awareness for Kaggle, to the stark reality of privacy attacks. Team member Narayanan subsequently consulted on the privacy of the $3m Heritage Health Prize dataset.
With a Berkeley group led by Dawn Song [report], I helped improve the security of Mozilla’s open-source development processes. While open-source projects tend to improve system security through the principle of ‘many eyes’, Mozilla was publishing security-related commits to the public Firefox web browser source repository, often a month before those commits would be automatically pushed to users. We trained a learning-based ranker to predict which commits were more likely security-related. An attacker could then easily sift through a few commits by hand to find zero-day exploits, on average a month prior to patching. As a result of our work Mozilla made security-related commits private until they were published as patches.
Note: view estimates are wildly approximate, sometimes “cumulative” overestimates.
I have co-authored submissions to 12 policy and legislation consultations and inquiries run by government departments and agencies. These responses highlight challenges and best-practice solutions in data privacy and AI.
Since arriving at the University of Melbourne Oct 2013, I have been awarded competitive funding (Cat 1–4) of $8.15m total, $5.73m as lead-CI, $2.59m on a per-CI basis. Funding includes:
Many more committees and working groups at departmental, faculty and university levels.