The 36th Chaos Communication Congress took place in Leipzig from 26-30 December, 2019. I’d always wanted to be part of the congress, but it is quite difficult to score a ticket if you’re not part of a hackerspace. Last year though, I was determined, and managed to get a ticket to 36c3 during the second presale.
I was quite excited, but also quite nervous, as I had no idea what to expect from this mega congress of 17000 people!
I was pleasantly surprised: I have not seen anything like this before, and you really need to be there to know what I am talking about. I do want to log my experience simply because I want to preserve it somewhere.
Be excellent to each other
With 17k people, and with the word chaos in the name, and with no central authority to police people around, you need some guiding principle that everyone abides by. The simple guideline, Be excellent to each other, worked really well. Of course there were some rough edges here and there, and I did hear stories of rude behaviour from my new friends, but overall, the event went pretty smoothly.
The large halls in the venue are divided up into blocks, and each block is transformed by like-minded hackers into assemblies. There were assemblies for local hacker spaces, for electronics/hardware hacking, and even a Perl hackers assembly. I learned there that there is a CCC Zürich chapter too, and that they’re visiting as part of the Swiss CCC assembly. So I had to go and say hi to them (Back in Zürich, I also went to one Chaostreff, their weekly meeting).
Talks and workshops I found interesting
The real meat of the conference are the self-organized sessions, since you can watch most talks (modulo technical glitches) online. Nevertheless, I attended a good mix of workshops and talks, as I wanted to just soak in as much Congress as I could, without forcing myself into any one kind of thing.
The premise of this talk is that good security today research not only requires the traditional security related skills, but also engineering expertise to scale solutions.
This one was fascinating. Go watch the talk (and other talks from the LHC team from previous Congresses). The highlights:
Charged particles are bent along the curvature of the collider using powerful dipole electromagnets with a flux density of 8.3 Tesla. This requires about 118500 Amps of current.
This current is carried using a superconducting strip. The magnet coils are also superconducting, and operate at 1.7 K (outer space is 2.9 K!).
Bunches of charged particles are accelerated together. Since these particles carry the same kind of charge, they repel each other, and tend to “unbunch”. This is fixed by placing focusing magnets along the circumference of the tube. Note that this focusing needs to be done both along and perpendicular to the direction of travel of the particles.
Hong Kongers have persistently protested against a proposed bill that will allow extradition to China. In spite of various atrocities inflicted by the government at the protesters, the protests are alive. This fascinating talk is an analysis of techniques used in these protests (and to an extent, also earlier protests).
To me, the idea of “Climbing the same mountain, in different ways” really hit home. Authoritarian governments try to create division among protesters by highlighting one particular group’s methods. This causes groups to oppose each other, often losing sight of the common goal. For example, during the Indian struggle for independence from British rule, Gandhi’s non-violent non-cooperation movement was a fundamentally different approach compared to the more direct, offensive approach taken by the likes of Bhagat Singh. Both groups had the same high level goal, but Gandhi’s group was opposed to any form of violence, even if it was in direct response to British atrocities. Hong Kongers have figured out how to totally support fellow protesters, even if their means to the common goal are varied and sometimes opposed.
Another interesting bit was how art and technology is used. Entire day’s worth of events are condensed into sharp memes and infographics (also translated into many Western languages) so that everyone in the world can understand the protesters’ side of the story. This is very important in face of state held press conferences that constantly condemn the protesters. Telegram groups are used to dump locations of police cars, water cannons, tear gas, and other artifacts of interest. These are picked up and overlaid on a map so citizens can plan both protests and more ordinary things like trips to airports with ease.
I also could draw parallels to the situation in India where, as I write this, there are protests against the proposed Citizenship Amendment Bill. It is striking how similar the government’s response has been to that of the Hong Kong authorities. But the Indian government is not shy to exercise drastic measures like Internet shutdowns. This is not in small part due to the fact that a large fraction of educated Indians (around me, my sample) seem to not have a problem with the government interfering at this level, as long as it advertises its intent as nationalistic.
A short, sweet talk about the challenges of healthcare data, with emphasis on the privacy implications.
Various data protection regulations speak of the distinction between narrow and broad consents that individuals can give on their health data. However, on the ground, individuals waiting for emergency treatment will seldom read the fine print on admission forms, much less push back on the broad-consent-by-default clauses in there.
Then there are challenges around healthcare data produced by different organizations. Mixed units, different names for treatments, etc. are rampant, and hence data must be compiled with care. There is also confusion around the ground truth – for a given condition, what diagnosis was “the right one”? Without robust ground truth, it is difficult to use such data for machine learning purposes.
Speaking of ML, fairness is a tricky issue. Healthcare data is incomplete and far from representative. Models trained on this data are going to be biased. It is important then to quantify this bias using an appropriate definition of “fairness”. For example, one might say that a fair model has equal false positive rates across groups defined by sensitive attributes. Another definition of fairness might assert that protected characteristics have no effect on classification. But then I ask why include them at all? And even if we identify and remove all sensitive attributes, can we guarantee that the model won’t learn these attributes anyway from other attributes? In that sense, can we ever be confident that we’ve identified all sensitive attribute combinations?
The search for anonymous data
This talk was unfortunately not recorded, but the premise is that conventional methods of scrubbing datasets clean of private and personally identifying information is not sufficient today.
Even techniques like k-anonymity fall short (susceptible to differential attacks). The speaker proposes a move from released datasets to a query-response paradigm, where all data is accessed only via a trusted gateway, which responds only if the query is guaranteed to not leak private information (within the bounds promised by the system). For example, there was mention of the Diffix, which is a system that works this way.
There was a lot of buzz about quantum computing in the Congress. I think this is because the CCC attracts security focussed individuals and professionals, and guess what promises to break RSA crypto one day?
From a guy who is trying to build a quantum computer in his “backyard”, to an analysis of the current landscape of the field, to hallroom discussions around the topic, I found a lot of people taking interest in the topic. This also prompted me to finally pick up a book to study the basics of QM as applicable to QC.
Some parties have challenged the recent quantum supremacy claims from Google. However, the general understanding in the CCC seemed to be that even if Google’s experiment used an “artificial” problem, the fact that 53 qubits can be controlled in this manner is a great validation of the very idea of quantum computing.
Random stuff I learned about
- Programming fractals
- The golden ratio and how it manifests in nature
- The Forth programming language
- Proprietary firmware has a whitelist of network cards you are allowed to install!
- Bayesian thinking (how to respond to evidence systematically, courtesy of LessWrong).