All posts by msoos

SAT Solvers as Smart Search Engines

Satisfiability problem solvers, or SAT solvers for short, try to find a solution to decidable, finite problems such as cryptography, planning, scheduling, and the like. They are very finely tuned engines that can be looked at in two main ways . One is to see them as proof generators, where the SAT solver is building a proof of unsatisfiability as it runs, i.e. it tries to prove that there is no solution to the problem. Another way is to see SAT solvers as smart search engines. In this blog post, I’ll take this latter view and try to explain why I think intermediary variables are important. So, for the sake of argument, let’s forget that SAT solvers sometimes restart the search (forgetting where they were before) and learn clauses (cutting down the search space and remembering where not go again). Let’s just pretend all they do is search.

Searching

The CryptoMiniSat SAT solver used to be able to generate graphs that show how a search through the search space went. Search spaces in these domains are exponential in size, say, 2^n in case there are n variables involved. I don’t have the search visualization code anymore but below is an example of such a search tree. The search starts at the very top not far from the middle, it descends towards the bottom left, then iteratively backtracks all the way to the top, and then descends towards the bottom right. Every pentagon at the bottom of a line is a place where the SAT solver backtracked. Observe that it never goes all the way back to the top — except once, when the top assignment needs to be flipped. Instead, it only goes back some way, partially unassigning variables. The bottom right corner is where the solution is found after many-many partial backtracks and associated partial unassignements:

What I want you to take away from this graph is the following: the solver iteratively tries to set a variable a value, calculates forward, and if it doesn’t work, it will partially backtrack, flip its value to its opposite, then descend again.

Brute force search vs. SAT solving

Trying one value and then trying the other sounds suspiciously like brute force search. Brute force search does exactly that, in a systematic and incredibly efficient way. We can build highly specialized executables and even hardware, to perform this task. If you look at e.g. Bitcoin mining, you will see a lot of specialized hardware, ASICs, doing brute-force search. And if you look at rainbow tables, you’ll see a lot of bit slicing.

So why waste our time doing all this fancy value propagation and backtracking when we could use a much more effective, systematic search system? The answer is, if you generated your problem description wrongly, then basically, for no good reason, and you are probably better off doing brute-force search. But if you did well, then a SAT solver can perform a significantly better search than brute-force. The trick lies in intermediary variables, and partial value assignments.

Partial value assignments

So let’s say that your brute force engine is about to check one input variable setting. It sets the input variables, runs the whole algorithm, and computes the output. The output is wrong though. Here is where things go weird. The brute force engine now completely erases its state, takes another input and runs the whole algorithm again. 

So, brute force does the whole calculation again, starting from a clean state, every time. What we have to recognize is that this is actually a design choice. Another design choice is to calculate what variables were affected by one of the input bits, unset these variables, flip the input bit value, and continue running the calculation. This has the following requirements:

  1. A way to quickly determine which intermediate values depend on which other ones so we can unset variables and know which intermediate, already calculated, dependent variables also need to be unset.
  2. A way to quickly unset variables
  3. A good set of intermediary values so we can keep as much state of the calculation as possible

If you think about it, the above is what SAT solvers do, well mostly. In fact, they do (1) only partially: they allow variables only to be unset in reverse chronological order. Calculating and maintaining a complete dependency graph seems too expensive. So we unset more variables than we need to. But we can unset them quickly and correctly and we compensate for the lack of correct dependency check in (1) by caching polarities. This caches the independent-but-nevertheless-unset variables’ values and then hopes to reassign them later to the correct value. Not perfect, but not too shabby either.

Modeling and intermediary variables

To satisfy requirement (3) one must have a good set of intermediary variables in the input problem (described in DIMACS format), so the SAT solver can both backtrack and evaluate partially. Unfortunately, this is not really in the hands of the SAT solver. It is in the hands of the person describing the problem. Modeling is the art of transforming a problem that is usually expressed in natural language (such as “A person cannot be scheduled to be on a night shift twice in a row”) into a problem that can be given to a SAT solver.

Modeling has lots of interesting constraints, one of which I often hear and I am confused by: that it should minimize the number of variables. Given the above, that SAT solvers can be seen at as partial evaluation engines that thrive on the fact that they can partially evaluate and partially backtrack, why would anyone try to minimize the number of variables? If the solver has no intermediary variables to backtrack to, the solver will simply backtrack all the way to the beginning every time, thus becoming a really bad brute-force engine that incidentally tracks a dependency graph and is definitely non-optimized for the task at hand.

Some final thoughts

In the above I tried to take a premise, i.e. that SAT solvers are just search engines, and ran with it. I don’t think the results are that surprising. Of course, nothing is black-and-white. Having hundreds of millions of variables in your input is not exactly optimal. But minimizing the number of variables given to a SAT solver at the expense of expressive intermediate variables is a huge no-no.

Bosphorus, an ANF and CNF simplifier and converter

I am happy to finally release a piece of work that I have started many years ago at Security Research Labs (many thanks to Karsten Nohl there). Back in the days, it helped us to break multiple real-world ciphers. The released system is called Bosphorus and has been released with major, game-changing work by Davin Choo and Kian Ming A. Chai from DSO National Laboratories Singapore and great help by Kuldeep Meel from NUS. The paper will be published at the DATE 2019 conference.

ANFs and CNFs

Algebraic Normal Form is a form that is used by most cryptographers to describe symmetric ciphers, hash algorithms, and lately a lot of post-quantum asymmetric ciphers. It’s a very simple notation that basically looks like this:

x1 ⊕ x2 ⊕ x3 = 0
x1 * x2 ⊕ x2 * x3 + 1 = 0

Where “⊕” represents XOR and “*” represents the AND operator. So the first line here is an XOR of binary variables x1, x2 and x3 and their XOR must be equal to 0. The second line means that “(x1 AND x2) XOR (x2 AND x3)” must be equal to 1. This normal form allows to see a bunch of interesting things. For example, it allows us to see the so-called “maximum degree” of the set of equations, where the degree is the maximum number of variables AND-ed together in one line. The above set of equations has a maximum degree of 2, as (x1*x2) is of degree 2. Degrees can often be a good indicator for the complexity of a problem.

What’s good about ANFs is that there are a number of well-known algorithms to break problems described in them. For example, one can do (re)linearization and Gauss-Jordan elimination, or one could run Grobner-basis algorithms such as F4/F5 on it. Sometimes, the ANFs can also be solved by converting them to another normal form, Conjunctive Normal Form (CNF), used by SAT solvers. The CNF normal form looks like:

x1 V x2 V x3
-x1 V x3

Where x1, x2 and x3 are binary variables, “V” is the logical OR, and each line must be equal to TRUE. Using CNF is interesting, because the solvers used to solve them, SAT solvers, typically provide a different set of trade-offs for solving than ANF problem solvers. SAT solvers tend to use more CPU time but a lot less memory, sometimes making problems viable to solve in the “real world”. Whereas sometimes breaking of a cipher is enough to be demonstrated on paper, it also happens that one wants to break a cipher in the real world.

Bridging and Simplifying

Bosphorus is I believe a first of its kind system that allows ANFs to be simplified using both CNF- and ANF-based systems. It can also convert between the two normal forms and can act both as an ANF and a CNF preprocessor, like SatELite (by Een and Biere) was for CNF. I believe this makes Bosphorus unique and also uniquely useful, especially if you are working on ANFs.

Bosphorus uses an iterative architecture that performs the following set of steps, either until it runs out of time or until fixedpoint:

  1. Replace variables and propagate constants in the ANF
  2. Run limited Extended Linarization (XL) and inject back unit and binary XORs
  3. Run limited ElimLin and inject back unit and binary XORs
  4. Convert to CNF, run a SAT solver for a limited number of conflicts and inject back unit and binary (and potentially longer) XORs

In other words, the system is an iterative simplifier/preprocessor that invokes multiple reasoning systems to try to simplify the problem as much as possible. It can outright solve the system, as most of these reasoning systems are complete, but the point is to run them only to a certain limit and inject back into the ANF the easily “digestible” information. The simplified ANF can then either be output as an ANF or a CNF.

Bosphorus can also take a CNF as input, perform the trivial transformation of it to ANF and then treat it as an ANF. This allows the CNF to be simplified using techniques previously unavailable to CNF systems, such as XL.

ANF to CNF Conversion

I personally think that ANF-to-CNF conversion is actually not that hard, and that’s why there hasn’t been too much academic effort devoted to it. However, it’s an important step without which a lot of opportunities would be missed.

The implemented system contains a pretty advanced ANF-to-CNF converter, using Karnaugh tables through Espresso, XOR cutting, monomial reuse, etc. It should give you a pretty optimal CNF for all ANFs. So you can use Bosphorus also just as an ANF-to-CNF converter, though it’s so much more.

Final Thoughts

What I find coolest about Bosphorus is that it can simplify/preprocess ANF systems so more heavyweight ANF solvers can have a go at them. Its ANF simplification is so powerful, it can even help to solve some CNFs by lifting them to ANF, running the ANF simplifiers, converting it back to CNF, and solving that(!). I believe our initial results, published in the paper, are very encouraging. Further, the system is in a ready-to-use state: there is a Docker image, the source should build without a hitch, and there is even a precompiled Linux binary. Good luck using it, and let me know how it went!

How Approximate Model Counting Works

Approximate model counting allows to count the number of solutions (or “models”) to propositional satisfiability problems. This problem seems trivial at first given a propositional solver that can find a single solution: find one solution, ban it, ask for another one, ban it, etc. until all solutions are counted. The issue is that sometimes, the number of solutions is 2^50 and so counting this way is too slow. There are about 2^266 atoms in the universe, so counting anywhere near that is impossible using this method.

Exact Counting

Since we cannot count 1-by-1, we are then left with trying to count in some smarter way. There are a bunch of methods to count exactly, the simplest is to cut the problem on a variable, count when the variable is True, count when the variable is False, recursively, and add them all up. Given caching of components that recur while “cutting” away, this can be quite successful, as implemented by sharpSAT (see Marc Thurley’s paper).

These counters can scale quite well, but have some downsides. In particular, when the memory runs out, the cache needs to be groomed, sometimes resorting back to 1-by-1 counting, which we know will fail as there is no way 2^200 can be counted 1-by-1 in any reasonable amount of time. The caching systems used are smart, though, retaining last-used entries when the cache needs to be groomed. However, sometimes this grooming algorithm can lead to cyclic behaviour that effectively simulates 1-by-1 counting.

Approximate Counting

What Chakraborty, Meel and Vardi did in their paper, was to create a counter that counts not exactly, but “probably approximately correctly”. This jumble of terms basically means: there is a certain probability that the counting is correct, within a given threshold. We can both improve the probability and the threshold given more CPU time spent. In practical terms, the probability can be set to be over 99.99% and the threshold can be set to be under 20%, still beating exact counters. Note that 20% is not so much, as e.g. 2^60*1.2 = 2^(60.3).

A Galton Box

So what’s the trick? How can we approximately count and give guarantees about the count? The trick is in fact quite simple. Let’s say you have to count balls, and there are thousands of them. One way to do it is to count 1-by-1. But, if you have a machine that can approximately half the number of balls you have, it can be done a lot faster: you half the balls, then check if you have at least 5 doing 1-by-1 counting. If you do, you half them again, and check if you have at least 5, etc. Eventually, let’s say you halved it 11 times and now you are left with 3 balls. So approximately, you must have had 3*2^11 = 6144 balls to begin with. In the end you had to execute the 1-by-1 count only 11*5+3+1 =59 times — a lot less than 6144! This is the idea used by ApproxMC.

Approximately Halving Using XORs

The “approximate halving” function used by ApproxMC is the plain XOR function, populated with variables picked with 50% probability. So for example if we have variables v1…v10, we pick each variable with 50% probability and add them into the same XOR. Let’s say we picked v1,v2,v5 and v8. The XOR would then be: v1⊕v2⊕v5⊕v8=1. This XOR is satisfied if an odd number of variables from the set {v1,v2,v5,v8} are 1. This intuitively forbids about half the solutions. The “intuitively” part of course is not enough, and if you read the original paper you will find the rigorous mathematical proof for the approximate halving of solutions this XOR function provides.

OK, so all we need to do is add these XORs to our original problem and we are done! This sounds easy, but there is a small hurdle and there is a big hurdle associated with this.

The small hurdle is that the original problem is a CNF, i.e. a conjunction of disjunctions, looking like “(v1 OR v2) AND (v2 OR not v3 OR not v4) AND…”. The XOR obviously does not look like this. The straightforward translation of XOR into CNF is exponential, so we need to add some variables to cut them smaller. It’s not that hard to figure this out and eventually add all the XORs into the CNF.

XORs, CDCL, and Gauss-Jordan Elimination

The larger hurdle is that once the XORs are in the CNF using the translation, the CNF becomes exponentially hard to solve using standard CDCL as used in most SAT solvers. This is because Gauss-Jordan elimination is exponentially hard for the standard CDCL to perform — but we need Gauss-Jordan elimination because the XORs will interact with each other, as we expect there to be many of them. Without being able to resolve the XORs with each other efficiently and derive information from them, it will be practically impossible to solve the CNF.

The solution is to tightly integrate Gauss-Jordan elimination into the solving process. CryptoMiniSat was the first solver to do this tight integration (albeit only for Gaussian elimination, which is sufficient). Other solvers have followed, in particular the work by Tero Laitinen and work by Cheng-Shen Han and Jie-Hong Roland Jiang. CryptoMiniSat currently uses the code by Han and Jiang after some cleanup and updates.

The latest work on ApproxMC and CryptoMiniSat has added one more thing besides the tight integration of the CDCL cycle: it now allows in- and pre-processing to occur while the XORs are inside the system. This brought some serious speedups as pre- and inprocessing are important factors in SAT solving. In fact, all modern SAT solvers strongly depend on them being active and working.

Concluding Remarks

We have gone from what model counting is, through how approximate counting works from a high-level perspective, all the way to the detail of running such a system inside a modern SAT solver. In case you want to try it out, you can do it by downloading the pre-built binaries or building it from source.

ApproxMCv3, a modern approximate model counter

This blogpost and its underlying work has been brewing for many years, and I’m extremely happy to be able to share it with you now. Kuldeep Meel and myself have been working very hard on speeding up approximate model counting for SAT and I think we have made real progress. The research paper, accepted at AAAI-19 is available here. The code is available here (release with static binary here). The main result is that we can solve a lot more problems than before. The speed of solving is orders(!) of magnitude faster than the previous best system:

Background

The idea of approximate model counting, originally by Chakraborty, Meel and Vardi was a huge hit back in 2013, and many papers have followed it, trying to improve its results. All of them were basically tied to CryptoMiniSat, the SAT solver that I maintain, as all of them relied on XOR constraints being added to the regular CNF of a typical SAT problem.

So it made sense to examine what CryptoMiniSat could do to improve the speed of approximate counting. This time interestingly coincided with me giving up on XORs in CryptoMiniSat. The problem was the following. A lot of new in- and preprocessing systems were being invented, mostly by Armin Biere et al, and I quickly realised that I simply couldn’t keep adding them, because they didn’t take into account XOR constraints. They handled CNF just fine, but not XORs. So XORs became a burden, and I removed them in versions 3 and 4 of CryptoMiniSat. But there was need, and Kuldeep made it very clear to me that this is an exciting area. So, they had to come back.

Blast-Inprocess-Recover-Destroy

But how to both have and not have XOR constraints? Re-inventing all the algorithms for XORs was not a viable option. The solution I came up with was a rather trivial one: forget the XORs during inprocessing and recover them after. The CNF would always remain the source of truth. Extracting all the XORs after in- and preprocessing would allow me to run the Gauss-Jordan elimination on the XORs post-recovery. So I can have the cake and eat it too.

The process is conceptually quite easy:

  1. Blast all XORs into clauses that are in the input using intermediate variables. I had all the setup for this, as I was doing Bounded Variable Addition  (also by Biere et al.) so I didn’t have to write code to “hide” these additional variables.
  2. Perform pre- or inprocessing. I actually only do inprocessing nowadays (as it has faster startup time). But preprocessing is just inprocessing at the start ;)
  3. Recover the XORs from the CNF. There were some trivial methods around. They didn’t work as well as one would have hoped, but more on that later
  4. Run the CDCL and Gauss-Jordan code at the same time.
  5. Destroy the XORs and goto 2.

This system allows for everything to be in CNF form, lifting the XORs out when necessary and then forgetting them when it’s convenient. All of these steps are rather trivial, except, as I later found out, recovery.

XOR recovery

Recovering XORs sounds like a trivial task. Let’s say we have the following clauses

This is conceptually equivalent to the XOR v1+v2+v3=1. So recovering this is trivial, and has been done before, by Heule in particular, in his PhD thesis. The issue with the above is the following: a stronger system than the above still implies the XOR, but doesn’t look the same. Let me give an example:

This is almost equivalent to the previous set of clauses, but misses a literal from one of the clauses. It still implies the XOR of course. Now what? And what to do when missing literals mean that an entire clause can be missing? The algorithm to recover XORs in such cases is non-trivial. It’s non-trivial not only because of the complexity of how many combinations of missing literals and clauses there can be (it’s exponential) but because one must do this work extremely fast because SAT solvers are sensitive to time.

The algorithm that is in the paper explains all the bit-fiddling and cache-friendly data layout used along with some fun algorithms that I’m sure some people will like. We even managed to use compiler intrinsics to use target-specific assembly instructions for hamming weight calculation. It’s a blast. Take a look.

The results

The results, as shown above, speak for themselves. Problems that took thousands of seconds to solve can now be solved under 20. The reason for such incredible speedup is basically the following. CryptoMiniSatv2 was way too clunky and didn’t have all the fun stuff that CryptoMiniSatv5 has, plus the XOR handling was incorrect, loosing XORs and the like. The published algorithm solves the underlying issue and allows CNF pre- and inprocessing to happen independent of XORs, thus enabling CryptoMiniSatv5 to be used in all its glory. And CryptoMiniSatv5 is fast, as per the this year’s SAT Competition results.

Some closing words

Finally, I want to say thank you to Kuldeep Meel who got me into the National University of Singapore to do the work above and lots of other cool work, that we will hopefully publish soon. I would also like to thank the National Supercomputing Center Singapore  that allowed us to run a ton of benchmarks on their machines, using at least 200 thousand CPU hours to make this paper. This gave us the chance to debug all the weird edge-cases and get this system up to speed where it beats the best exact counters by a wide margin. Finally, thanks to all the great people I had the chance to meet and sometimes work with at NUS, it was a really nice time.

 

The Cult of Security/Privacy By Design

I have been reading a lot of privacy literature lately and the more I read, the more I got frustrated with the “Privacy by Design” motto. It somehow was not right, and made me realise that “Security by Design” motto is similarly hyped and is just as misguided. Let me explain.

Why security/privacy wasn’t baked in at the start

First of all, the basics: “X by Design” means that security/privacy is baked into the product from the inception and design through to implementation,  and all the way to operation. This of course sounds great, and one can easily see that trying to retroactively implement security or privacy features/requirements into an already existing product is a lot harder than baking it in all from the start. Where I think this starts to fall apart is that often, the reason for something being only “an afterthought” is not that it wasn’t actually considered while e.g. conceptualizing the product. It’s because it was considered but either:

  1. There weren’t any people available who could help i.e. the security/privacy personnel were busy, expensive or too complicated to acquire/hire/consult or
  2. It wasn’t considered important to involve the right people/team because the project/product was supposed to be small/unimportant and security/privacy-irrelevant but over time it grew to be important/large

In other words, it was either resource constraints or legitimate business reason not to spend resources on something (that was meant to be) small/irrelevant.

Reminiscing of the past

What I see in a lot of these “X by Design” discussions is a set of technical people reminiscing about how awesome it would have been if e.g. Google built their search engine in 1998 with security/privacy in mind. Yes, it would have been, but the engineers writing the code had no idea if it would take off and had neither the time nor the money to spend on expensive consultants and long consultation periods to bake security/privacy in. The same can be said of many other startups’ products and of most projects in large companies that grow to become products of their own.

If security and privacy engineers would have to be involved in all smaller projects then innovation would seriously suffer, because there are simply too few of us and we would  take too long to take a sufficiently good look at everything (that might not take off anyway). So, since we cannot be involved at the beginning of every project, the best we can hope for is to fix it afterwards — there is no point in reminiscing in how awesome it would have been if we were there when Twitter started in 2006. We weren’t there, it now took off and they are offering value to their customers, bringing in revenue which they are (hopefully) using to fix their security/privacy issues. I think it’s too optimistic to assume they could have afforded the delay-to-market and the price attached to doing it “right” from the start. Most probably, they couldn’t.

Living in the present

All in all, I think a most of the “X by Design” is wishful thinking with some sermons attached to it, telling long tales of how things could have been so much better, had the right security/privacy people been consulted. What these tales often forget to tell is that when the idea was born, and the project/product was created, the situation was not even remotely amenable to such an intervention. Hence, I think it’s time to let go for the most part — we should concentrate on doing what needs to be done. We should make sure we have solid processes and good, flexible patterns for fixing already existing products.