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Latest Card Not Present Fraud Stats - Australia

The Australian Payments Network (formerly the Australian Payments Clearing Association, APCA) releases http://www.apca.com.au/payment-statistics/fraud-statistics"card fraud statistics every six months for the preceding 12m period. For over a decade now, Lockstep has been monitoring these figures, plotting the trend data and analysing what the industry is doing - and not doing - about Card Not Present fraud. Here is our summary for the most recent calendar year 2016 stats.

CNP trends pic to CY 2016

Total card fraud climbed another 17% from 2015 to 2016; Card Not Present (CNP) fraud was up 15% to $417 million, representing 82% of all card fraud.

CNP fraud is enabled by the difficulty merchants (and merchant servers) have telling the difference between original cardholder details and stolen data. Criminals procure stolen details in enormous volumes and replay them against vulnerable shopping sites.

A proper foundational fix to replay attack is easily within reach, which would re-use the same cryptography that solves skimming and carding, and would restore a seamless payment experience for card holders. Apple for one has grasped the nettle, and is using its Secure Element-based Apple Pay method (established now for card present NFC payments) for Card Not Present transactions, in the app.

See also my 2012 paper Calling for a Uniform Approach to Card Fraud Offline and On" (PDF).


The credit card payments system is a paragon of standardisation. No other industry has such a strong history of driving and adopting uniform technologies, infrastructure and business processes. No matter where you keep a bank account, you can use a globally branded credit card to go shopping in almost every corner of the world. The universal Four Party settlement model, and a long-standing card standard that works the same with ATMs and merchant terminals everywhere underpin seamless convenience. So with this determination to facilitate trustworthy and supremely convenient spending in every corner of the earth, it’s astonishing that the industry is still yet to standardise Internet payments. We settled on the EMV standard for in-store transactions, but online we use a wide range of confusing and largely ineffective security measures. As a result, Card Not Present (CNP) fraud is growing unchecked.

This article argues that all card payments should be properly secured using standardised hardware. In particular, CNP transactions should use the very same EMV chip and cryptography as do card present payments.

With all the innovation in payments leveraging cryptographic Secure Elements in mobile phones, perhaps at last we will see CNP payments modernised for web and mobile shopping.

Posted in Payments

Yet another anonymity promise broken

In 2016, the Australian government released, for research purposes, an extract of public health insurance data, comprising the 30-year billing history of ten percent of the population, with medical providers and patients purportedly de-identified. Melbourne University researcher Dr Vanessa Teague and her colleagues famously found quite quickly that many of the providers were readily re-identified. The dataset was withdrawn, though not before many hundreds of copies were downloaded from the government website.

The government’s responses to the re-identification work were emphatic but sadly not positive. For one thing, legislation was written to criminalize the re-identification of ostensibly ‘anonymised’ data, which would frustrate work such as Teague’s regardless of its probative value to ongoing privacy engineering (the bill has yet to be passed). For another, the Department of Health insisted that no patient information has been compromised.

It seems less ironic than inevitable that in fact the patients’ anonymity was not to be taken as read. In follow-up work released today, Teague, with Dr Chris Culnane and Dr Ben Rubinstein, have now published a paper showing how patients in that data release may indeed be re-identified.

The ability to re-identify patients from this sort of Open Data release is frankly catastrophic. The release of imperfectly de-identified healthcare data poses real dangers to patients with socially difficult conditions. This is surely well understood. What we now need to contend with is the question of whether Open Data practices like this deliver benefits that justify the privacy risks. That’s going to be a trick debate, for the belief in data science is bordering on religious.

It beggars belief that any government official would promise "anonymity" any more. These promises just cannot be kept.

Re-identification has become a professional sport. Researchers are constantly finding artful new ways to triangulate individuals’ identities, drawing on diverse public information, ranging from genealogical databases to social media photos. But it seems that no matter how many times privacy advocates warn against these dangers, the Open Data juggernaut just rolls on. Concerns are often dismissed as academic, or being trivial compared with the supposed fruits of research conducted on census data, Medicare records and the like.

In "Health Data in an Open World (PDF)" Teague et al warn (not for the first time) that "there is a misconception that [protecting the privacy of individuals in these datasets] is either a solved problem, or an easy problem to solve” (p2). They go on to stress “there is no good solution for publishing sensitive unit-record level data that protects privacy without substantially degrading the usefulness of the data" (p3).

What is the cost-benefit of the research done on these data releases? Statisticians and data scientists say their work informs government policy, but is that really true? Let’s face it. "Evidence based policy" has become quite a joke in Western democracies. There are umpteen really big public interest issues where science and evidence are not influencing policy settings at all. So I am afraid statisticians need to be more modest about the practical importance of their findings when they mount bland “balance” arguments that the benefits outweigh the risks to privacy.

If there is a balance to be struck, then the standard way to make the calculation is a Privacy Impact Assessment (PIA). This can formally assess the risk of “de-identified” data being re-identified. And if it can be, a PIA can offer other, layered protections to protect privacy.

So where are all the PIAs?

Open Data is almost a religion. Where is the evidence that evidence-based policy making really works?

I was a scientist and I remain a whole-hearted supporter of publicly funded research. But science must be done with honest appraisal of the risks. It is high time for government officials to revisit their pat assertions of privacy and security. If the public loses confidence in the health system's privacy protection, then some people with socially problematic conditions might simply withdraw from treatment, or hold back vital details when they engage with healthcare providers. In turn, that would clearly damage the purported value of the data being collected and shared.

Big Data-driven research on massive public data sets just seems a little too easy to me. We need to discuss alternatives to massive public releases. One option is to confine research data extracts to secure virtual data rooms, and grant access only to specially authorised researchers. These people would be closely monitored and audited; they would comprise a small set of researchers; their access would be subject to legally enforceable terms & conditions.

There are compromises we all need to make in research on human beings. Let’s be scientific about science-based policy. Let’s rigorously test our faith in Open Data, and let’s please stop taking “de-identification” for granted. It’s really something of a magic spell.

Posted in Big Data, Government, Privacy