Zero-Knowledge Cooperationpost by bryjnar · 2017-10-25T05:35:33.931Z · LW · GW · 7 comments
Security needs of decision algorithms and capitulation thresholds data is sensitive Zero knowledge cooperation histories source-code sharing Conclusion None 7 comments
A lot of ink has been spilled about how to get various decision algorithms to cooperate with each other. However, most approaches require the algorithm to provide some kind of information about itself to a potential cooperator.
Consider FDT, the hot new kid on the block. In order for FDT to cooperate with you, it needs to reason about how your actions are related to the algorithm that the FDT agent is running. The naive way of providing this information is simple: give the other agent a copy of your “source code”. Assuming that said code is analyzable without running into halting problem issues, this should allow FDT to work out whether it wants to cooperate with you.
Security needs of decision algorithms
However, just handing out your source code to anyone isn’t a great idea. This is because agents often want to pretend to have a different decision algorithm to the one that they really do.
Threats and capitulation thresholds
Many people adopt some version of the “don’t give in to threats” heuristic. The reasoning here is solid: while refusing to give in to a threat carries a penalty in the particular instance, being the kind of person who doesn’t give in to threats pays off in the long run because you receive fewer threats.
However, usually this will come with an escape hatch in case the stakes get too high. If I am trying to extort £100 from you by plausibly threatening to destroy the planet, you should just pay the money. Why is this okay? Well, threats of higher magnitude are usually harder to create, or carry higher costs for the threatener, so an agent can expect to face relatively few of them.
The absolute best thing for an agent, though, would be to have the reputation of never giving in to threats, no matter how high the cost, while actually being willing to give in to threats above some threshold. That way you never get threatened, but if by some ill luck you do face an enormous threat, you can still capitulate. In general, an agent would like their capitulation threshold to be perceived to be as high as possible, ideally infinity.
It is therefore in an agent’s interest to hide their capitulation threshold. If an adversary finds out what it is, and it’s something they can exceed easily, then they can exploit you, even if they only get one shot at threatening you.
This makes an agent’s capitulation threshold a piece of sensitive data, and keeping it secret is a security problem. Obviously just handing our your source code is right out, but an agent should also take other security precautions. To give a short list:
- Be cautious about talking about how you make decisions.
- Try to conceal any evidence of occasions when you have capitulated in the past.
- Avoid revealing information about your past behaviour in general, or at least follow glomarization rules.
- Avoid interacting with agents who appear to be probing for sensitive information (e.g. escalating threats).1
All data is sensitive
I’ve been talking about a capitulation threshold because it’s an easy example, but the history of computer security tells us that leaking any kind of information about your implementation can be dangerous. It’s best to just assume that attackers have magical attack powers and hide as much as you can.
(Aside: what about FDT? Isn’t it immune to this kind of exploitation? Sure, but you’re not going to be running FDT, you’re going to be running some approximation to it. Imagine an agent running FDT-approx, which spends N cycles computing an approximation to FDT, then acts on that. The value of N is then sensitive data - knowing that could allow an adversary to construct a situation just complicated enough to lead to poor behaviour without being too costly to construct.)
But if we are operating in a context where revealing information about how we work is dangerous, how are we supposed to reveal enough about ourselves to be able to cooperate effectively?
Zero knowledge cooperation
We can borrow some ideas from security to help us here. Suppose that A wishes to convince B that they are likely to cooperate in an upcoming game. What A actually wants to convey to B is that precise fact: they are likely to cooperate. Ideally they would reveal no other knowledge at all. What we want is a zero-knowledge proof of A’s willingness to cooperate.
This seems like it could be a whole subfield of work, but here are a few suggestions.
One way for A provide evidence that they are likely to cooperate is to provide historical evidence that they cooperated in similar situations in the past. However, this is sensitive information, so ideally they would like it not to be public.
So let’s suppose that after a game has played out, the results are made public, but identifying each agent with a unique token, for which they can provide a zero-knowledge proof of ownership.
Thus A can reveal that they were a participant in a previous game, providing evidence towards their future actions, but without providing this knowledge to anyone except B.
Secret source-code sharing
The most convincing evidence A could provide would be a copy of its source code.2 However, they don’t want B to end up with that, but only the derived information that it “looks trustworthy”.
To that end we can run the following protocol.3
First, A provides B with a copy of A’s source code, encrypted using A’s secret key and a fully homomorphic encryption scheme.4 Then B runs (homomorphically) a program (the Validator) on the source code that validates a property (in this case “trustworthiness”), and outputs 0 or 1 depending on whether the property holds. Crucially, the Validator also signs this output with B’s secret key.
Now what B actually receives is an encrypted version of the Validator’s output, so B has to return this to A for decryption. A can validate that the decrypted response matches an agreed upon pattern (is 0 or 1, not, say, the full source code), in which case they pass it back to B, along with the signature. Since A cannot forge B’s signature, B trusts that this really is the output of the Validator.
I think this is a zero-knowledge proof, since B cannot replicate the process for someone else without having A’s secret key, and a log of the transactions does not rule out B having provided A with the response beforehand. Informally, the “surprising” part of the protocol is A producing a token with B’s signature, but this is only surprising to B, and could be easily faked for a third part if A and B were colluding.
I think the situation looks surprisingly good here. A cautious agent should be able to get by with revealing the minimum amount of information about itself. The biggest problem is the currently prohibitive cost of FHE, but I’m optimistic that that will come down over time.
Even if you have good security procedures you still want to do this. You should install fail2ban even if you think SSH is secure. ↩
There is, of course, the small matter of proving that the source code you provided is the code you’re actually running, but this is independent of the security problem. ↩
This protocol actually allows B to extract one bit of information of any kind from A’s secret data, so could be used for other purposes too. As far as I know it’s novel. ↩
FHE is prohibitively expensive to perform at the moment, but we’re looking for a possibility proof here. Yes, I’m using a very large hammer to crack this nut. ↩
Comments sorted by top scores.