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Wednesday, 27 February 2019

History of and thoughts on the 14200 HIV related leak in MoH Singapore

The Singapore health sector has been hit with a second information leak in a few months. After the hacking episode at SingHealth, there has been a willful disclosure of confidential information from the Ministry of Health (MoH). The case is made more serious because of the potential impact of the leak on peoples’ lives. It contains the names and contact details of 14,200 people who have been found HIV positive in Singapore and some of their contacts.

While the current case only became public in January 2019, this is part of a much longer story, where the 2 principal actors are Mr Brochez a US citizen, and Dr Ler, a Singapore citizen and who briefly headed the National Public Health of the MoH.

Many of you may know, I am quite serious about data privacy, so I decided to try and put the whole story in perspective. This is a story 12 (or more) years in the making, it really does read like a novel, but my aim is to help think about data security/confidentiality then and now.

I hope the infographic above is readable, it wasn’t easy trying to fit so much information into 1 single page.

The story starts in 2007 with Mr Brochez and Dr Ler meeting online, liking each other’s internet personalities
Mr Brochez moved to Singapore in January 2008; as part of the requirements for an employment pass required for foreigners to work in Singapore, he was required to submit to an HIV test. People who are found to have HIV positive would not be allowed to work in Singapore.
Mr Brochez first took the HIV test in march 2008, at the Singapore Anti Tuberculosis Association clinic using a fake Bahamian passport. He tests HIV positive.
Dr Ler draws his own blood, Mr Brochez turns up at the “My family clinic” in Commonwealth where Dr Ler is working, and Dr Ler submits his own blood as that of Mr Brochez for testing. This sample test HIV negative and Mr Brochez is granted the employment pass (EP). He works in private practice.
In September 2008, Mr Brochez applies to teach at Temasek Poly and is hired.
On Jan 1 2010, The New Paper publishes an article on Mr Brochez, where he explains his genius, how he was brought up by his famous doctor mother, was the youngest in Princeton (at 13) went to Vanderbilt (where he obtained 2 Masters) among other claims. At a later stage these were all shown to be fake.
In February 2011, Mr Brochez is granted a Personal Employment Pass (PEP) by the Ministry of Manpower (MoM)
In March 2012, Dr Ler starts his stint as Head of the National Public Health at the MoH.
In November 2012, Mr Brochez informs an MoH Director that Dr Ler showed him screenshots of the database of people who have tested HIV positive, and informed someone else he was HIV positive. MoH launches an investigation, but Mr Brochez subsequently does not cooperate.
Dr Ler’s role as head of National Public Health at MoH ends in May 2013.
In October 2013, MoM receives information that Mr Brochez is HIV positive, and asks him to cancel his PEP by 8 November 2013. Mr Brochez replies that he will prove that he is HIV negative.
On November 22 2013, Dr Ler again passes off his blood as that of Mr Brochez for him to retain his PEP.
Dr Ler and Mr Brochez deny again that the blood tested was not that of Mr Brochez.
Dr Ler resigns from MoH in January 2014.
Mr Brochez and Dr Ler get married in April 2014 in New York.
In May 2016, Mr Brochez is found in possession of 'Ketaminised Cannabis'. During the search his education certificates were also found, and they were found to be forged.
In May 2016 he MoH also makes a police report against Mr Brochez after learning he has a list of people who tested HIV positive in Singapore and their contacts.
Dr Ler admits he used his own blood to substitute that of Mr Brochez in the HIV tests that were negative.
In June 2016, Mr Brochez is remanded for numerous fraud charges including lying about his HIV status, and drug related charges.
Dr Ler is charged under the Penal Code and the Official Secrets Act; for OSA the charge was relating to not keeping possession of a thumb drive with details of the HIV registry.
In July 2017, the government disables the use of non-authorised portable storage devices as part of a government wide tightening of security.
In April 2018, Mr Brochez finishes his sentence and is deported.
In May 2018, MoH makes a police report after learning that Mr Brochez still has a copy of the registry.
On January 22 2019, the police informs MoH that a list of people who tested HIV positive in Singapore and their contacts has be leaked online.
On January 24 2019, the MoH confirms that the data was for the registry as at January 2013.
MoH and the police work to disable access to the information leaked online
MoH confirms the leak to the public on January 28 2019.

What do you think of the case?\

First of all this case is very different from the SingHealth case where people hacking was involved, and the first response was to sweep things under the carpet and not even report the breach. In this case, I feel, the first priority of MoH was to inform the people affected and provide them with the support they need. This must be a very traumatic time for them.

Secondly, I think this is a story of official blindness or stupidity.

The unnamed private practice that employed Mr Brochez in 2008 must have reviewed his education certificates before deciding to hire him.
The MoM too must have checked his education certificates in 2008; Vanderbilt does not have millions of Masters Graduates.
Temasek Poly also reviewed the education certificates in September 2008, and must have interviewed Mr Brochez thoroughly; as a lecturer he would have the power to help mould the minds of many young people.
The New paper also swallowed the stories of Mr Brochez hook, line and sinker.

Ok, now to the elephant in the room…

MoH was told since November 2012 that Dr Ler had, in his possession screenshots of the HIV registry. While it has been argued by the MoH that people in Dr Ler’s role needed to be able to download the data (more on that later), I fail to see why screen shots shown to people who are not authorized to see the database do not set alarm bells ringing.Yes, Mr Brochez decided not to cooperate with the enquiry, but in view of the risk (the damage caused to people on the registry if their details were leaked), a more serious and sustained effort should have been made.But then, that brings up the point; if data is protected by the Official Secrets Act, and that data relates to peoples personal details and contact details, what legitimate use can one really have to mass download the records? Wasn’t the usage of the database tracked?
In May 2016, when presumably a copy of the list was discovered when searching the apartment, why didn’t the authorities consider that there may have been copies not on-site? At the least, couldn’t the sentencing of Mr Brochez be subject to his having relinquished all the copies and undertaking not to use them?
Why did the MoH not institute increased protection for the database and wait until the government-wide initiative to do so? MoH had already known for sure of the issues since at worse June 2016; Dr Ler was charged with regards to OSA, but still the disabling of downloads to unauthorized storage only happened in July 2017.

In sum

I think MoH did the right thing in focusing in the potential fall-out of the leak and worked to disable access to the information and counselling the people potentially directly impacted. However, the whole saga revealed a very loose approach to data security: allowing and even justifying mass download of the database (I actually think the intent would be much much worse if it was downloaded 1 record at a time) by saying it was required by the role Dr Ler was playing as Head of National Public Health, to me, shows that the people who created and thought of the use of the database did not take into account the risks.

On the other hand, I find it really interesting that the original employer of Mr Brochez who sponsored his original EP (unless this was an organization somehow linked to Dr Ler – this is something I could not find out), the MoM, Temasek Poly, all failed to see that his academic credentials were forged.

Latest Development
In a latest development, Mr Brochez has also made available a list of 13 HIV positive people who were due for medical check-up at Changi Prison on March 28 2018 (15). Mr Brochez finished his sentence and was deported in April 2018.

Whether it is the prison service, Parkway Shenton (who is contracted to carry out the tests), it is really amazing how little consideration they give to the data they are guardians of. How can a person, who is in prison, have access to a list of fellow patients, their identities and identity card numbers…

In a facebook post over the weekend (16), Mr Brochez also maintained that he has tried to inform the authorities of the leaks since 2012, and claims that neither he, not Dr Ler, were the ones who copied the information, but rather a lawyer in Singapore with whom his husband was having an affair.

The police and prison service responded (17).

Needless to say that this saga is not likely to end so soon, but what I hope had ended is data leaks due to bad (allowing mass download) or lax policies (not verifying educational certs, not tightening procedures immediately after leaks found).

16. http://theindependent.sg/mikhy-brochez-now-claims-he-did-not-steal-hiv-records-and-that-he-was-only-infected-with-hiv- when-he-was-raped-in-singapore -prison /

https://www.singaporenewsgazette.com/joint-spf-sps-statement-in-response-to-allegations-made-in-mikhy-brochezs-facebook-post-2 /

Sunday, 27 January 2019

A true "data scientist"

All around the internet, in many wine bars and high end cigar joints but fewer kopitiams, you may hear people discussing about “data science” and how it can change the world. You have seen many diagrams telling you what a “data scientist” is, or should be, and I have been as guilty promoting ideas such as there is no such thing as a “data scientist” but the roles and impact of a “data scientist” are more likely to be achieved by a “data science team”.

Recently I came across someone who has successfully built “data science teams” and not only that, but managed to get real life results out of them while leading an organisation – imagine a “head of data science team” as “COO”.

A man, yup it is a male human, who has shown how to use huge volumes of unstructured data to derive extremely detailed insights, and implement strategies to take advantage of these insights in highly competitive, real life, multi-million dollar arenas across the world.

To recap, what is a “data science team”

To me “data science” involves all the steps from collecting the data, preparing it so it can be analysed, analysing the data, engaging in discovery/predictions based on the data, and importantly acting on the discoveries/predictions and implementing a feedback loop.

In most organisations, this effort may even be the product of more than one department/team, with the “data science team” not responsible for directly managing the implementation but only in providing the insights and measuring the impact.

To me, discovery and prediction is important because this is what differentiates reporting/business intelligence from analytics/”data science”. The tools used for discovery/prediction are less important as long as these are based on data.

And to add, one item that would make an analysis truly a “big data” issue is the use of unstructured data, photos, movies, sounds files…

Personality traits

Someone recently commented that in order to be called a “data scientist” you need to be really mad about analysis. And to a large extend I agree.

I believe in passion, and extreme passion can be seen as a form of madness.

Interestingly, last week I attended a management retreat kind of do. It was interesting; they used the Myers Briggs 16 personalities. The idea was to teach us how to related to each other, to understand how different people work, and help make communication easier.

During the high level explanation, the coach/moderator explained to us that people who are “sensing” as opposed to people who are “Intuitive” are more likely to be moved by data, and prefer the use of data to make decisions. On the other hand, “intuitive” people are more likely to be interested in the big picture, rather than the nitty gritty of data.

Since according to the test I am more “intuitive” than “sensing”, I brought this question up over drinks. And the answer, which seemed reasonable at the time (and still does) was that deeper analysis tends to be an art where “intuitive” people do well. (FYI I did prefer to be called a “data artist” rather than a “data scientist” and do have 2 or 3 pieces in the “Art of Analytics”(1))…

So to me people in the “data science” field should be passionate about what they are doing, especially getting result from their work. If there is no outcome, it should just be academic work, not real life.

So to sum up, a “data scientist” in my view should be passionate about analysing data and about applying it in the real world.

Ok, enough beating around the bush…

Who is this “data scientist”, this leader of a “data science team”?

First let me tell you some of what that team does, it is a huge team of 20 by the way

Collect all information available from public sources, including unstructured data
Supplements it with specialist provided data
Breaks down each dataset into identifiable and manageable chunks, clusters, aspect
Classifies every aspect of the data
Learns the patterns that hide in the data for each aspect/chunk
Predicts the most likely result of a pattern and devises the best response
Translates the analysis into visuals, charts…
Makes each action plan digestible by the people who actually act

Hence in terms of the data science process:

Data Collection/Pipelines	Preparation/ Storage/Transformation	Data Analysis	Visualisation/ Applications
Collect all information available from public sources, including unstructured data	Breaks down each dataset into identifiable and manageable chunks, clusters, aspect	Learns the patterns that hide in the data for each aspect/chunk	Translates the analysis into visuals, charts…
Supplements it with specialist provided data	Classifies every aspect of the data	Predicts the most likely result of a pattern and devises the best response	Makes each action plan digestible by the people who actually act

The data science team covers all areas in the “data science” process; and they do more.

Not only does the leader of this “data science team” propose the plan that is predicted to bring success, but he also manages the implementation on the ground, provides live feedback and thereby allows his team to update the analysis in real time.

And after the fact, the team conducts post-mortem analysis.

So who is this leader of a “data science team” that I am using as example?

Marcelo Bielsa

If you have not read the transcript of his 70 minute presentation on how he prepares for each game, including friendlies, please do so (2).

Let me just list some highlights of his obsession with analysing data and extracting insights:
Of each opponent, we watched all of the games from the 2017-2018 season… the 51 games of Derby county, we watched them. The analysis of each game takes 4 hours of work. Why did we do that? Because we think this is professional behaviour
The other thing we do is point out the chances to score, the half chances to score and which team dominates every 5 minutes… That’s why it takes 4 hours to analyse each game.
We also do video analysis where we register goals, chances, and that’s why it takes four hours to analyse the game.
It’s a process, a way to get to know the opponent.
We can express all these parameters with this graphic

He then talks about understanding the structure and the patterns (patterns within context, sound familiar?)

They played 31 games. 49% they used a 4-3-3- system with #8 on the right… In 22% a 4-3-3- but with the #8 on the left. Same for 4-2-1-3, with #8 on the right and left. They also used structures, 3%, 2% but they are not significant.
These structures give you fixed priorities and we here have the minutes and the games to understand why Derby changes the system and when.
I would explain that all this information, I don’t memorise it. All the information that this report has corresponds to the questions I have asked myself over the last 30 years

A true SME, putting all his knowledge, defining the metric, parameters, analytical methods, then executing the analysis over 51 football games of 90 minutes each, with each analysis taking 4 hours.

He even looks at various forms of data

This is the tactical analysis. This is the document. Have a look. The analysis of chances, goals, domination… We see in each segment of 5 minutes, who dominates and if they create chances.
Now let’s look at the video of this game. We know what this player will do when he raises both hands…

And by classifying the different phases, he can predict the patterns

As you can see, you have 40 minutes of offensive action from Derby from 31 games. When you see 41 minutes of attack, you see what is the path for the opponent to attack.
And if you do the same thing analysing what were the chances conceded by Derby County, you see the defensive weaknesses.
We know if the made 184 corners, 33 were dangerous; we know qualitative data. We try to find weaknesses of the goalkeeper, where we can press. The players know about the opponent.

And creates further visualisations so the consumers of his analyses can get the most out of them

Our goal is to sum up in 7 or 8 minutes to show to the player ow Derby attack…
We also make a 7 or 8 minutes video showing their defensive weakness
The goal of this analysis is to allow our players to have an idea of the opponent in 15 minutes.

And drills further into individual players

To sum up, these are the players and we have the position which they played… we gather information for the systems, we analyse to see which player goes in which system.

Finally the passion bordering on obsession. There are stories (3) where even when coaching at a University, he watched 3,000 players to pick 20, or how he systematically travelled his country (Argentina) into 70 sections and arranged trials in each, travelling to each in his Fiat 147 so he could catch all the talent.

Here we have the analysis for all teams. This is for this season. This is for last season. This is for friendlies. We went to watch the opponent (he has only been on the job this season)
I don’t need to watch a training session to know where they play. Why do I go? Because it’s not forbidden… ad even though watching an opponent is not useful, it allows me to keep my anxiety low

And the understanding of the limits of analysis

I was very sad to lose this game. When the game finished I sent it (the analysis of that days’ opponents) to Guardiola (the winning coach), this analysis expressing my admiration for him. He told me “you know more about Barcelona than me”. But it was useless because they scored three goals against us. I do this to feel well, I see that this information does not allow you to win games.

Ok, possibly this last trait may disqualify him; humble “data scientist”? mmm…

But how about tools you ask!?!

Is he using the cloud? Is it azure, aws, GCP, Alibaba, Huawei…?

Is all the data on Hadoop and is he using tensor-flow?

For sure he must be using Neural networks right?

Yes you are right.

They have 20 extremely complex neural networks with some interaction between them. The most trained neural network has around 30 years of data. This is the brain of Marcelo Bielsa.

Where is MY blue bucket?