Tag Archives: Opinions

I Have Been Appointed As E-Governance Minister of Bulgaria

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Last week the Bulgarian National assembly appointed the new government. I am one of the appointed ministers – a minister for electronic governance.

The portfolio includes digitizing registers and processes in all government institutions, reducing bureaucracy, electronic identity, cybersecurity, digital skills and more.

Thanks to all my readers for following this blog throughout the years. I will be sharing some digital policy details here from now on while I’m minister. That may include some technical articles, but they are unlikely to be developer-oriented.

I hope to make some important changes and put forward key ideas for e-governance and digital policy that can be used as an example outside my country (last time I was involved in public policy, I helped pass an “open source law”).

I’ve written a few articles about IT people looking for challenges – not just technical challenges. And I think that’s a great challenge where I’ll have to put all my knowledge and skills to work for the common good.

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Simple Things That Are Actually Hard: User Authentication

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You build a system. User authentication is the component that is always there, regardless of the functionality of the system. And by now it should be simple to implement it – just “drag” some ready-to-use authentication module, or configure it with some basic options (e.g. Spring Security), and you’re done.

Well, no. It’s the most obvious thing and yet it’s extremely complicated to get right. It’s not just login form -> check username/password -> set cookie. It has a lot of other things to think about:

  • Cookie security – how to make it so that a cookie doesn’t leak or can’t be forged. Should you even have a cookie, or use some stateless approach like JWT, use SameSite lax or strict?
  • Bind cookie to IP and logout user if IP changes?
  • Password requirements – minimum length, special characters? UI to help with selecting a password?
  • Storing passwords in the database – bcrypt, scrypt, PBKDF2, SHA with multiple iterations?
  • Allow storing in the browser? Generally “yes”, but some applications deliberately hash it before sending it, so that it can’t be stored automatically
  • Email vs username – do you need a username at all? Should change of email be allowed?
  • Rate-limiting authentication attempts – how many failed logins should block the account, for how long, should admins get notifications or at least logs for locked accounts? Is the limit per IP, per account, a combination of those?
  • Captcha – do you need captcha at all, which one, and after how many attempts? Is Re-Captcha an option?
  • Password reset – password reset token database table or expiring links with HMAC? Rate-limit password reset?
  • SSO – should your service should support LDAP/ActiveDirectory authentication (probably yes), should it support SAML 2.0 or OpenID Connect, and if yes, which ones? Or all of them? Should it ONLY support SSO, rather than internal authentication?
  • 2FA – TOTP or other? Implement the whole 2FA flow, including enable/disable and use or backup codes; add option to not ask for 2FA for a particular device for a period of time? Configuring subset of AD/LDAP users to authenticate based on certain group memberships?
  • Force 2FA by admin configuration – implement time window for activating 2FA after a global option is enabled?
  • Login by link – should the option to send a one-time login link be email be supported?
  • XSS protection – make sure no XSS vulnerabilities exist especially on the login page (but not only, as XSS can steal cookies)
  • Dedicated authentication log – keep a history of all logins, with time, IP, user agent
  • Force logout – is the ability to logout a logged-in device needed, how to implement it, e.g. with stateless tokens it’s not trivial.
  • Keeping a mobile device logged in – what should be stored client-side? (certainly not the password)
  • Working behind proxy – if the client IP matters (it does), make sure the X-Forwarded-For header is parsed
  • Capture login timezone for user and store it in the session to adjust times in the UI?
  • TLS Mutual authentication – if we need to support hardware token authentication with private key, we should enable TLS mutual. What should be in the truststore, does the web server support per-page mutual TLS or should we use a subdomain, if there’s a load balancer / reverse proxy, does it support it and how to forward certificate details?
  • Require account activation or let the user login immediately after registration? Require account approval by back-office staff?
  • Initial password setting for accounts created by admins – generate initial password and force changing it on first login? Don’t generate password and start from a password reset flow?
  • Login anomalies – how to detect them and should you inform the user? Should you rely on 3rd party tools (e.g. a SIEM), or have such functionality built-in?

And that’s for the most obvious feature that every application has. No wonder it has been implemented incorrectly many, many times. The IT world is complex and nothing is simple. Sending email isn’t simple, authentication isn’t simple, logging isn’t simple. Working with strings and dates isn’t simple, sanitizing input and output isn’t simple.

We have done a poor job in building the frameworks and tools to help us with all those things. We can’t really ignore them, we have to think about them actively and take conscious, informed decisions.

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Integrity Guarantees of Blockchains In Case of Single Owner Or Colluding Owners

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The title may sound as a paper title, rather than a blogpost, because it was originally an idea for such, but I’m unlikely to find the time to put a proper paper about it, so here it is – a blogpost.

Blockchain has been touted as the ultimate integrity guarantee – if you “have blockchain”, nobody can tamper with your data. Of course, reality is more complicated, and even in the most distributed of ledgers, there are known attacks. But most organizations that are experimenting with blockchain, rely on a private network, sometimes having themselves as the sole owner of the infrastructure, and sometimes sharing it with just a few partners.

The point of having the technology in the first place is to guarantee that once collected, data cannot be tampered with. So let’s review how that works in practice.

First, we have two define two terms – “tamper-resistant” (sometimes referred to as tamper-free) and “tamper-evident”. “Tamper-resistant” means nobody can ever tamper with the data and the state of the data structure is always guaranteed to be without any modifications. “Tamper-evident”, on the other hand, means that a data structure can be validated for integrity violations, and it will be known that there have been modifications (alterations, deletions or back-dating of entries). Therefore, with tamper-evident structures you can prove that the data is intact, but if it’s not intact, you can’t know the original state. It’s still a very important property, as the ability to prove that data is not tampered with is crucial for compliance and legal aspects.

Blockchain is usually built ontop of several main cryptographic primitives: cryptographic hashes, hash chains, Merkle trees, cryptographic timestamps and digital signatures. They all play a role in the integrity guarantees, but the most important ones are the Merkle tree (with all of its variations, like a Patricia Merkle tree) and the hash chain. The original bitcoin paper describes a blockchain to be a hash chain, based on the roots of multiple Merkle trees (which form a single block). Some blockchains rely on a single, ever-growing merkle tree, but let’s not get into particular implementation details.

In all cases, blockchains are considered tamper-resistant because their significantly distributed in a way that enough number of members have a copy of the data. If some node modifies that data, e.g. 5 blocks in the past, it has to prove to everyone else that this is the correct merkle root for that block. You have to have more than 50% of the network capacity in order to do that (and it’s more complicated than just having them), but it’s still possible. In a way, tamper resistance = tamper evidence + distributed data.

But many of the practical applications of blockchain rely on private networks, serving one or several entities. They are often based on proof of authority, which means whoever has access to a set of private keys, controls what the network agree on. So let’s review the two cases:

  • Multiple owners – in case of multiple node owners, several of them can collude to rewrite the chain. The collusion can be based on mutual business interest (e.g. in a supply chain, several members may team up against the producer to report distorted data), or can be based on security compromise (e.g. multiple members are hacked by the same group). In that case, the remaining node owners can have a backup of the original data, but finding out whether the rest were malicious or the changes were legitimate part of the business logic would require a complicated investigation.
  • Single owner – a single owner can have a nice Merkle tree or hash chain, but an admin with access to the underlying data store can regenerate the whole chain and it will look legitimate, while in reality it will be tampered with. Splitting access between multiple admins is one approach (or giving them access to separate nodes, none of whom has access to a majority), but they often drink beer together and collusion is again possible. But more importantly – you can’t prove to a 3rd party that your own employees haven’t colluded under orders from management in order to cover some tracks to present a better picture to a regulator.

In the case of a single owner, you don’t even have a tamper-evident structure – the chain can be fully rewritten and nobody will understand that. In case of multiple owners, it depends on the implementation. There will be a record of the modification at the non-colluding party, but proving which side “cheated” would be next to impossible. Tamper-evidence is only partially achieved, because you can’t prove whose data was modified and whose data hasn’t (you only know that one of the copies has tampered data).

In order to achieve tamper-evident structure with both scenarios is to use anchoring. Checkpoints of the data need to be anchored externally, so that there is a clear record of what has been the state of the chain at different points in time. Before blockchain, the recommended approach was to print it in newspapers (e.g. as an ad) and because it has a large enough circulation, nobody can collect all newspapers and modify the published checkpoint hash. This published hash would be either a root of the Merkle tree, or the latest hash in a hash chain. An ever-growing Merkle tree would allow consistency and inclusion proofs to be validated.

When we have electronic distribution of data, we can use public blockchains to regularly anchor our internal ones, in order to achieve proper tamper-evident data. We, at LogSentinel, for example, do exactly that – we allow publishing the latest Merkle root and the latest hash chain to Ethereum. Then even if those with access to the underlying datastore manage to modify and regenerate the entire chain/tree, there will be no match with the publicly advertised values.

How to store data on publish blockchains is a separate topic. In case of Ethereum, you can put any payload within a transaction, so you can put that hash in low-value transactions between two own addresses (or self-transactions). You can use smart-contracts as well, but that’s not necessary. For Bitcoin, you can use OP_RETURN. Other implementations may have different approaches to storing data within transactions.

If we want to achieve tamper-resistance, we just need to have several copies of the data, all subject to tamper-evidence guarantees. Just as in a public network. But what a public network gives is is a layer, which we can trust with providing us with the necessary piece for achieving local tamper evidence. Of course, going to hardware, it’s easier to have write-only storage (WORM, write once, ready many). The problem with it, is that it’s expensive and that you can’t reuse it. It’s not so much applicable to use-cases that require short-lived data that requires tamper-resistance.

So in summary, in order to have proper integrity guarantees and the ability to prove that the data in a single-owner or multi-owner private blockchains hasn’t been tampered with, we have to send publicly the latest hash of whatever structure we are using (chain or tree). If not, we are only complicating our lives by integrating a complex piece of technology without getting the real benefit it can bring – proving the integrity of our data.

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Hypotheses About What Happened to Facebook

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Facebook was down. I’d recommend reading Cloudflare’s summary. Then I recommend reading Facebook’s own account on the incident. But let me expand on that. Facebook published announcements and withdrawals for certain BGP prefixes which lead to removing its DNS servers from “the map of the internet” – they told everyone “the part of our network where our DNS servers are doesn’t exist”. That was the result of a backbone self-inflicted failure due to a bug in the auditing tool that checks whether the commands executed aren’t doing harmful things.

Facebook owns a lot of IPs. According to RIPEstat they are part of 399 prefixes (147 of them are IPv4). The DNS servers are located in two of those 399. Facebook uses a.ns.facebook.com, b.ns.facebook.com, c.ns.facebook.com and d.ns.facebook.com, which get queries whenever someone wants to know the IPs of Facebook-owned domains. These four nameservers are served by the same Autonomous System from just two prefixes – and Of course “4 nameservers” is a logical construct, there are probably many actual servers behind that (using anycast).

I wrote a simple “script” to fetch all the withdrawals and announcements for all Facebook-owned prefixes (from the great API of RIPEstats). Facebook didn’t remove itself from the map entirely. As CloudFlare points out, it was just some prefixes that are affected. It can be just these two, or a few others as well, but it seems that just a handful were affected. If we sort the resulting CSV from the above script by withdrawals, we’ll notice that and are the pretty high up (alongside 185.89 and 123.134 with a /24, which are all included in the /23). Now that perfectly matches Facebook’s account that their nameservers automatically withdraw themselves if they fail to connect to other parts of the infrastructure. Everything may have also been down, but the logic for withdrawal is present only in the networks that have nameservers in them.

So first, let me make three general observations that are not as obvious and as universal as they may sound, but they are worth discussing:

  • Use longer DNS TTLs if possible – if Facebook had 6 hour TTL on its domains, we may have not figured out that their name servers are down. This is hard to ask for such a complex service that uses DNS for load-balancing and geographical distribution, but it’s worth considering. Also, if they killed their backbone and their entire infrastructure was down anyway, the DNS TTL would not have solved the issue.
  • We need improved caching logic for DNS. It can’t be just “present or not”; DNS caches may keep “last known good state” in case of SERVFAIL and fallback to that. All of those DNS resolvers that had to ask the authoritative nameserver “where can I find facebook.com” knew where to find facebook.com just a minute ago. Then they got a failure and suddenly they are wondering “oh, where could Facebook be?”. It’s not that simple, of course, but such cache improvement is worth considering. And again, if their entire infrastructure was down, this would not have helped.
  • Have a 100% test coverage on critical tools, such as the auditing tool that had a bug. 100% test coverage is rarely achievable in any project, but in such critical tools it’s a must.

The main explanation is the accidental outage. This is what Facebook engineers explain in the blogpost and other accounts, and that’s what seems to have happened. However, there are alternative hypotheses floating around, so let me briefly discuss all of the options.

  • Accidental outage due to misconfiguration – a very likely scenario. These things may happen to everyone and Facebook is known for it “break things” mentality, so it’s not unlikely that they just didn’t have the right safeguards in place and that someone ran a buggy update. The scenarios why and how that may have happened are many, and we can’t know from the outside (even after Facebook’s brief description). This remains the primary explanation, following my favorite Hanlon’s razor. A bug in the audit tool is absolutely realistic (btw, I’d love Facebook to publish their internal tools).
  • Cyber attack – It cannot be known by the data we have, but this would be a sophisticated attack that gained access to their BGP administration interface, which I would assume is properly protected. Not impossible, but a 6-hour outage of a social network is not something a sophisticated actor (e.g. a nation state) would invest resources in. We can’t rule it out, as this might be “just a drill” for something bigger to follow. If I were an attacker that wanted to take Facebook down, I’d try to kill their DNS servers, or indeed, “de-route” them. If we didn’t know that Facebook lets its DNS servers cut themselves from the network in case of failures, the fact that so few prefixes were updated might be in indicator of targeted attack, but this seems less and less likely.
  • Deliberate self-sabotage1.5 billion records are claimed to be leaked yesterday. At the same time, a Facebook whistleblower is testifying in the US congress. Both of these news are potentially damaging to Facebook reputation and shares. If they wanted to drown the news and the respective share price plunge in a technical story that few people understand but everyone is talking about (and then have their share price rebound, because technical issues happen to everyone), then that’s the way to do it – just as a malicious actor would do, but without all the hassle to gain access from outside – de-route the prefixes for the DNS servers and you have a “perfect” outage. These coincidences have lead people to assume such a plot, but from the observed outage and the explanation given by Facebook on why the DNS prefixes have been automatically withdrawn, this sounds unlikely.

Distinguishing between the three options is actually hard. You can mask a deliberate outage as an accident, a malicious actor can make it look like a deliberate self-sabotage. That’s why there are speculations. To me, however, by all of the data we have in RIPEStat and the various accounts by CloudFlare, Facebook and other experts, it seems that a chain of mistakes (operational and possibly design ones) lead to this.

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Digital Transformation and Technological Utopianism

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Today I read a very interesting article about the prominence of Bulgarian hackers (in the black-hat sense) and virus authors in the 90s, linking that to the focus on technical education in the 80s, lead by the Bulgarian communist party in an effort to revive communism through technology.

Near the end of the article I was pleasantly surprised to read my name, as a political candidate who advocates for digital e-government and transformation of the public sector. The article then ended with something that I’m in deep disagreement with, but that has merit, and is worth discussing (and you can replace “Bulgaria” with probably any country there):

Of course, the belief that all the problems of a corrupt Bulgaria can be solved through the perfect tools is not that different to the Bulgarian Communist Party’s old dream that central planning through electronic brains would create communism. In both cases, the state is to be stripped back to a minimum

My first reaction was to deny ever claiming that the state would be stripped back to a minimum, as it will not (risking to enrage my libertarian readers), or to argue that I’ve never claimed there are “perfect tools” that can solve all problems, nor that digital transformation is the only way to solve those problems. But what I’ve said or written has little to do with the overall perception of techno-utopianism that IT people-turned-policy makers are usually struggling with.

So I decided to clearly state what e-government and digital transformation of the public sector is about.

First, it’s just catching up to the efficiency of the private sector. Sadly, there’s nothing visionary about wanting to digitize paper processes and provide services online. It’s something that’s been around for two decades in the private sector and the public sector just has to catch up, relying on all the expertise accumulated in those decades. Nothing grandiose or mind-boggling, just not being horribly inefficient.

When the world grows more complex, legislation and regulation grows more complex, the government gets more and more functions and more and more details to care about. There are more topics to have policy about (and many to take an informed decision to NOT have a policy about). All of that, today, can’t rely on pen-and-paper and a few proverbial smart and well-intentioned people. The government needs technology to catch up and do its job. It has had the luxury to not have competition and therefore it lagged behind. When there are no market forces to drive the digital transformation, what’s left is technocratic politicians. This efficiency has nothing to do with ideology, left or right. You can have “small government” and still have it inefficient and incapable of making sense of the world.

Second, technology is an enabler. Yes, it can help solve the problems with corruption, nepotism, lack of accountability. But as a tool, not as the solution itself. Take open data, for example (something I’ve been working on five years ago when Bulgaria jumped to the top of the EU open data index). Just having the data out there is an important effort, but by itself it doesn’t solve any problem. You need journalists, NGOs, citizens and a general understanding in society what transparency means. Same for accountability – it’s one thing to have every document digitized, every piece of data – published and every government official action leaving an audit trail; it’s a completely different story to have society act on those things – to have the institutions to investigate, to have the public pressure to turn that into political accountability.

Technology is also a threat – and that’s beyond the typical cybersecurity concerns. It poses the risk of dangerous institutions becoming too efficient; of excessive government surveillance; of entrenched interests carving their ways into the digital systems to perpetuate their corrupt agenda. I’m by no means ignoring those risks – they are real already. The Nazis, for example, were extremely efficient in finding the Jewish population in the Netherlands because the Dutch were very good at citizen registration. This doesn’t mean that you shouldn’t have an efficient citizen registration system. It means that it’s not good or bad per se.

And that gets us to the question of technological utopianism, of which I’m sometimes accused (though not directly in the quoted article). When you are an IT person, you have a technical hammer and everything may look like a binary nail. That’s why it’s very important to have a glimpse on humanities sides as well. Technology alone will not solve anything. And my blockchain skepticism is a hint in that direction – many blockchain enthusiasts are claiming that blockchain will solve many problems in many areas of life. It won’t. At least not just through clever cryptography and consensus algorithms. I once even wrote a sci-fi story about exactly the aforementioned communist dream of a centralized computer brain that solves all social issues while people are left to do what they want. And argued that no matter how perfect it is, it won’t work in a non-utopian human world. In other words, I’m rather critical of techno-utopianism as well.

The communist party, according to the author, saw technology as a tool by which the communist government would achieve its ideological goal.

My idea is quite different. First, technology necessary for “catching up” of the public sector, and second, I see technology as an enabler. What for – whether it’s for accountability or surveillance, fight with corruption or entrenching corruption even further – it’s our role as individuals, as society, and (in my case) as politicians, to formulate and advocate for. We have to embed our values, after democratic debate, into the digital tools (e.g. by making them privacy-preserving). But if we want to have good governance, and to be good at policy-making in the 21st century, we need digital tools, fully understanding their pitfalls and without putting them on a pedestal.

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Every Serialization Framework Should Have Its Own Transient Annotation

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We’ve all used dozens of serialization frameworks – for JSON, XML, binary, and ORMs (which are effectively serialization frameworks for relational databases). And there’s always the moment when you need to exclude some field from an object – make it “transient”.

So far so good, but then comes the point where one object is used by several serialization frameworks within the same project/runtime. That’s not necessarily the case, but let me discuss the two alternatives first:

  • Use the same object for all serializations (JSON/XML for APIs, binary serialization for internal archiving, ORM/database) – preferred if there are only minor differences between the serialized/persisted fields. Using the same object saves a lot of tedious transferring between DTOs.
  • Use different DTOs for different serializations – that becomes a necessity when scenarios become more complex and using the same object becomes a patchwork of customizations and exceptions

Note that both strategies can exist within the same project – there are simple objects and complex objects, and you can only have a variety of DTOs for the latter. But let’s discuss the first option.

If each serialization framework has its own “transient” annotation, it’s easy to tweak the serialization of one or two fields. More importantly, it will have predictable behavior. If not, then you may be forced to have separate DTOs even for classes where one field differs in behavior across the serialization targets.

For example the other day I had the following surprise – we use Java binary serialization (ObjectOutputStream) for some internal buffering of large collections, and the objects are then indexed. In a completely separate part of the application, objects of the same class get indexed with additional properties that are irrelevant for the binary serialization and therefore marked with the Java transient modifier. It turns out, GSON respects the “transient” modifier and these fields are never indexed.

In conclusion, this post has two points. The first is – expect any behavior from serialization frameworks and have tests to verify different serialization scenarios. And the second is for framework designers – don’t reuse transient modifiers/annotations from the language itself or from other frameworks, it’s counterintuitive.

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