Phew! This is loooonnnng but excellent! Comments in-line!
On 4/2/20 10:54 AM, George Kadianakis wrote:i
Hello list,
hope everyone is safe and doing well!
I present you an initial draft of a proposal on PoW-based defences for onion services under DoS.
The proposal is not finished yet and it needs tuning and fixing. There are many places marked with XXX and TODO around the proposal that should be addressed.
The important part is that looking at the numbers it does seem like this proposal can work as a concept and serve its intended purpose. The most handwavey parts of the proposal right now are [INTRO_QUEUE] and [POW_SECURITY] and if this thing fails in the end, it's probably gonna be something that slipped over there. Hence, we should polish these sections before we proceed with any sort of engineering here.
In any case, I decided to send it to the list even in premature form, so that it can serve as a stable point of reference in subsequent discussions. It can also be found in my git repo: https://github.com/asn-d6/torspec/tree/pow-over-intro
Cheers and stay safe!
Filename: xxx-pow-over-intro-v1 Title: A First Take at PoW Over Introduction Circuits Author: George Kadianakis Created: 2 April 2020 Status: Draft
- Abstract
This proposal aims to thwart introduction flooding DoS attacks by introducing a dynamic Proof-Of-Work protocol that occurs over introduction circuits.
- Motivation
So far our attempts at limiting the impact of introduction flooding DoS attacks on onion services has been focused on horizontal scaling with Onionbalance, optimizing the CPU usage of Tor and applying congestion control using rate limiting. While these measures move the goalpost forward, a core problem with onion service DoS is that building rendezvous circuits is a costly procedure both for the service and for the network. If we ever hope to have truly reachable global onion services, we need to make it harder for attackers to overload the service with introduction requests.
This proposal achieves this by allowing onion services to specify an optional dynamic proof-of-work scheme that its clients need to participate in if they want to get served.
With the right parameters, this proof-of-work scheme acts as a gatekeeper to block amplification attacks by attackers while letting legitimate clients through.
1.1. Threat model [THREAT_MODEL]
1.1.1. Attacker profiles [ATTACKER_MODEL]
This proposal is written to thwart specific attackers. A simple PoW proposal cannot defend against all and every DoS attack on the Internet, but there are adverary models we can defend against.
Let's start with some adversary profiles:
"The script-kiddie"
The script-kiddie has a single computer and pushes it to its limits. Perhaps it also has a VPS and a pwned server. We are talking about an attacker with total access to 10 Ghz of CPU and 10 GBs of RAM. We consider the total cost for this attacker to be zero $."The small botnet"
The small botnet is a bunch of computers lined up to do an introduction flooding attack. Assuming 500 medium-range computers, we are talking about an attacker with total access to 10 Thz of CPU and 10 TB of RAM. We consider the upfront cost for this attacker to be about $400."The large botnet"
The large botnet is a serious operation with many thousands of computers organized to do this attack. Assuming 100k medium-range computers, we are talking about an attacker with total access to 200 Thz of CPU and 200 TB of RAM. The upfront cost for this attacker is about $36k.1.1.2. User profiles [USER_MODEL]
We have attackers and we have users. Here are a few user profiles:
"The standard web user"
This is a standard laptop/desktop user who is trying to browse the web. They don't know how these defences work and they don't care to configure or tweak them. They are gonna use the default values and if the site doesn't load, they are gonna close their browser and be sad at Tor. They run a 2Ghz computer with 4GB of RAM."The motivated user"
This is a user that really wants to reach their destination. They don't care about the journey; they just want to get there. They know what's going on; they are willing to tweak the default values and make their computer do expensive multi-minute PoW computations to get where they want to be."The mobile user"
This is a motivated user on a mobile phone. Even tho they want to read the news article, they don't have much leeway on stressing their machine to do more computation.We hope that this proposal will allow the motivated user to always connect where they want to connect to, and also give more chances to the other user groups to reach the destination.
1.1.3. The DoS Catch-22 [CATCH22]
This proposal is not perfect and it does not cover all the use cases. Still, we think that by covering some use cases and giving reachability to the people who really need it, we will severely demotivate the attackers from continuing the DoS attacks and hence stop the DoS threat all together. Furthermore, by increasing the cost to launch a DoS attack, a big class of DoS attackers will disappear from the map, since the expected ROI will decrease.
- System Overview
2.1. Tor protocol overview
+----------------------------------+ | |+-------+ INTRO1 +-----------+ INTRO2 +--------+ | |Client |-------->|Intro Point|------->| PoW |-----------+ | +-------+ +-----------+ |Verifier| | | +--------+ | | | | | | | | | +----------v---------+ | | |Intro Priority Queue| | +---------+--------------------+---+ | | | Rendezvous | | | circuits | | | v v v
The proof-of-work scheme specified in this proposal takes place during the introduction phase of the onion service protocol. It's an optional mechanism that only occurs if the service requires it. It can be enabled and disabled either through its torrc or through the control port.
In summary, the following steps are taken for the protocol to complete:
- Service encodes PoW parameters in descriptor [DESC_POW]
- Client fetches descriptor and computes PoW [CLIENT_POW]
- Client completes PoW and sends results in INTRO1 cell [INTRO1_POW]
- Service verifies PoW and queues introduction based on PoW effort [SERVICE_VERIFY]
2.2. Proof-of-work overview
2.2.1. Primitives
For our proof-of-work scheme we want to minimize the spread of resources between a motivated attacker and legitimate clients. This means that we are looking to minimize any benefits that GPUs or ACICs can offer to an attacker.
For this reason we chose argon2 [REF_ARGON2] as the hash function for our proof-of-work scheme since it's well audited and GPU-resistant and to some extend ASIC-resistant as well.
FWIW, I think we should also consider https://github.com/tevador/RandomX, which is based on argon2 plus some additional sauce, and comes as a library with C exports, pretty much tuned for our usecase.
The downside is that it is C++ itself, but if we use it as an optional external build dep (only Tor Browser and onion services need this thing.. relays do not), that should be fine.
As a password hash function, argon2 by default outputs 32 bytes of hash, and takes as primary input a message and a nonce/salt. For the purposes of this specification we will define an argon2() function as: uint8_t hash_output[32] = argon2(uint8_t *message, uint8_t *nonce)'.
See section [ARGON_PARAMS] for more information on the secondary inputs of argon2.
2.2.2. Dynamic PoW
DoS is a dynamic problem where the attacker's capabilities constantly change, and hence we want our proof-of-work system to be dynamic and not stuck with a static difficulty setting. Hence, instead of forcing clients to go below a static target like in Bitcoin to be successful, we ask clients to "bid" using their PoW effort. Effectively, a client gets higher priority the higher effort they put into their proof-of-work. This is similar to how proof-of-stake works but instead of staking coins, you stake work.
The benefit here is that legitimate clients who really care about getting access can spend a big amount of effort into their PoW computation, which should guarantee access to the service given reasonable adversary models. See [POW_SECURITY] for more details about these guarantees and tradeoffs.
- Protocol specification
3.1. Service encodes PoW parameters in descriptor [DESC_POW]
This whole protocol starts with the service encoding the PoW parameters in the 'encrypted' (inner) part of the v3 descriptor. As follows:
"pow-params" SP type SP seed-b64 SP expiration-time NL [At most once] type: The type of PoW system used. We call the one specified here "v1" seed-b64: A random seed that should be used as the input to the PoW hash function. Should be 32 random bytes encoded in base64 without trailing padding. expiration-time: A timestamp after which the above seed expires and is no longer valid as the input for PoW. It's needed so that the size of our replay cache does not grow infinitely. It should be set to an hour in the future (+- some randomness). {TODO: PARAM_TUNING} {XXX: Expiration time makes us even more susceptible to clock skews, but it's needed so that our replay cache refreshes. How to fix this? See [CLIENT_BEHAVIOR] for more details.}
We should also include the lowest difficulty successfully serviced from the queue recently (last N seconds of time), in this field, as a hint of the difficulty level that clients should shoot for, as a minimum.
This will save them from waiting for quite so many timeouts, and from doing too much needless work.
3.2. Client fetches descriptor and computes PoW [CLIENT_POW]
If a client receives a descriptor with "pow-params", it should assume that the service is expecting a PoW input as part of the introduction protocol.
In such cases, the client should have been configured with a specific PoW 'target' (which is a 32-byte integer similar to the 'target' of Bitcoin [REF_TARGET]). See [POW_SECURITY] for more information of how such a target should be set. For the purposes of this section, we will assume that the target has been set automatically by Tor, or the user configured it manually.
Now the client parses the descriptor and extracts the PoW parameters. It makes sure that the expiration-time has not expired and if it has, it needs to fetch a new descriptor.
To complete the PoW the client follows the following logic:
a) Client generates 'nonce' as 32 random bytes. b) Client derives 'seed' by decoding 'seed-b64'. c) Client computes hash_output = argon2(seed, nonce) d) Client interprets hash_output as a 32-byte big-endian integer. e) Client checks if int(hash_output) <= target. e1) If yes, success! The client uses 'hash_output' as the hash and 'nonce' and 'seed' as its inputs. e2) If no, fail! The client interprets 'nonce' as a big-endian integer, increments it by one, and goes back to step (c).At the end of the above procedure, the client should have a triplet (hash_output, seed, nonce) that can be used as the answer to the PoW puzzle. How quickly this happens depends solely on the 'target' parameter.
Nice. Clarification (as per dgoule's verification question):
The hard part here is finding the nonce that matches the target. Verification (running the hash once) should be easy.
RandomX does require (relatively) a lot of setup for the VM, etc, so we will need to be careful about preserving the right pieces of setup there. But, if we do that right, we're fine.
3.3. Client sends PoW in INTRO1 cell [INTRO1_POW]
Now that the client has an answer to the puzzle it's time to encode it into an INTRODUCE1 cell. To do so the client adds an extension to the encrypted portion of the INTRODUCE1 cell by using the EXTENSIONS field (see [PROCESS_INTRO2] section in rend-spec-v3.txt). The encrypted portion of the INTRODUCE1 cell only gets read by the onion service and is ignored by the introduction point.
We propose a new EXT_FIELD_TYPE value:
[01] -- PROOF_OF_WORKThe EXT_FIELD content format is:
POW_VERSION [1 byte] POW_SEED [32 bytes] POW_NONCE [32 bytes] POW_OUTPUT [32 bytes]where:
POW_VERSION is 1 for the protocol specified in this proposal POW_SEED is 'seed' from the section above POW_NONCE is 'nonce' from the section above POW_OUTPUT is 'hash_output' from the section above{XXX: do we need POW_VERSION? Perhaps we can use EXT_FIELD_TYPE as version} {XXX: do we need to encode the SEED? Perhaps we can ommit it since the service already knows it. But what happens in cases of desynch, if client has diff seed from service?} {XXX: Do we need to include the output? Probably not. The service has to compute it anyway during verification. What's the use?}
This will increase the INTRODUCE1 payload size by 99 bytes since the extension type and length is 2 extra bytes, the N_EXTENSIONS field is always present and currently set to 0 and the EXT_FIELD is 97 bytes. According to ticket #33650, INTRODUCE1 cells currently have more than 200 bytes available.
3.4. Service verifies PoW and handles the introduction [SERVICE_VERIFY]
When a service receives an INTRODUCE1 with the PROOF_OF_WORK extension, it should check its configuration on whether proof-of-work is required to complete the introduction. If it's not required, the extension SHOULD BE ignored. If it is required, the service follows the procedure detailed in this section.
3.4.1. PoW verification
To verify the client's proof-of-work the service extracts (hash_output, seed, nonce) from the INTRODUCE1 cell and MUST do the following steps:
- Make sure that the client's seed is identical to the active seed.
- Check the client's nonce for replays (see [REPLAY_PROTECTION] section).
- Verify that 'hash_output =?= argon2(seed, nonce)
If any of these steps fail the service MUST ignore this introduction request and abort the protocol.
If all the steps passed, then the circuit is added to the introduction queue as detailed in section [INTRO_QUEUE].
3.4.1.1. Replay protection [REPLAY_PROTECTION]
The service MUST NOT accept introduction requests with the same (seed, nonce) tuple. For this reason a replay protection mechanism must be employed.
The simplest way is to use a simple hash table to check whether a (seed, nonce) tuple has been used before for the actiev duration of a seed. Depending on how long a seed stays active this might be a viable solution with reasonable memory/time overhead.
If there is a worry that we might get too many introductions during the lifetime of a seed, we can use a Bloom filter as our replay cache mechanism. The probabilistic nature of Bloom filters means that sometimes we will flag some connections as replays even if they are not; with this false positive probability increasing as the number of entries increase. However, with the right parameter tuning this probability should be negligible and well handled by clients. {TODO: PARAM_TUNING}
3.4.2. The Introduction Queue [INTRO_QUEUE]
3.4.2.1. Adding introductions to the introduction queue
When PoW is enabled and a verified introduction comes through, the service instead of jumping straight into rendezvous, queues it and prioritizes it based on how much effort was devoted by the client to PoW. This means that introduction requests with high effort should be prioritized over those with low effort.
To do so, the service maintains an "introduction priority queue" data structure. Each element in that priority queue is an introduction request, and its priority is the effort put into its PoW:
When a verified introduction comes through, the service interprets the PoW hash as a 32-byte big-endian integer 'hash_int' and based on that integer it inserts it into the right position of the priority_queue: The smallest 'hash_int' goes forward in the queue. If two elements have the same value, the older one has priority over the newer one. {XXX: Is this operation with 32-bytes integers expensive? How to make cheaper?}
{TODO: PARAM_TUNING: If the priority queue is only ordered based on the effort what attacks can happen in various scenarios? Do we want to order on time+effort? Which scenarios and attackers should we examine here?}
{TODO: PARAM_TUNING: What's the max size of the queue? How do we trim it? Can we use WRED usefully?}
We should record the lowest difficulty level that was successfully serviced from the priority queue, and post it in the descriptor.
{TODO: PARAM_TUNING: Is lowest enough? Do we want to timebound that? How does it combine with options above? }
3.4.2.2. Handling introductions from the introduction queue [HANDLE_QUEUE]
The service should handle introductions by pulling from the introduction queue.
Similar to how our cell scheduler works, the onion service subsystem will poll the priority queue every 100ms tick and process the first 20 cells from the priority queue (if they exist). The service will perform the rendezvous and the rest of the onion service protocol as normal.
With this tempo, we can process 200 introduction cells per second. {XXX: Is this good?}
{TODO: PARAM_TUNING: STRAWMAN: This needs hella tuning. Processing 20 cells per 100ms is probably unmaintainable, since each cell is quite expensive: doing so involving path selection, crypto and making circuits. We will need to profile this procedure and see how we can do this scheduling better.}
{XXX: This might be a nice place to promote multithreading. Queues and pools are nice objects to do multithreading since you can have multiple threads pull from the queue, or leave stuff on the queue. Not sure if this should be in the proposal tho.}
I think we should only do multi-threading in v1 if it still fits in a single Tor release cycle. Otherwise slide it to a v1.5 or v2.
Onionbalance is a backstop option instead of multithreading.. The traffic analysis characteristics are not as good for this case, so if it helps in practice, we should be sure to do multithreading ASAP (but still maybe not v1? It really depends on how deep a rabbit-hole it is for this case).
- Attacker strategies [ATTACK_META]
Now that we defined our protocol we need to start tweaking the various knobs. But before we can do that, we first need to understand a few high-level attacker strategies to see what we are fighting against.
4.1.1. Total overwhelm strat
Given the way the introduction queue works (see [HANDLE_QUEUE]), a very effective strategy for the attacker is to totally overwhelm the queue processing by sending more high-effort introductions than the onion service can handle at any given tick.
To do so, the attacker would have to send at least 20 high-effort introduction cells every 100ms, where high-effort is a PoW which is above the estimated level of "the motivated user" (see [USER_MODEL]).
If the queue generates a libevent callback event when it has N entries, is this better? (Is such a callback hard to create?)
An easier attack for the adversary, is the same strategy but with introduction cells that are all above the comfortable level of "the standard user" (see [USER_MODEL]). This would block out all standard users and only allow motivated users to pass.
{XXX: What other attack strategies we should care about?}
- Parameter tuning [POW_SECURITY]
There are various parameters in this system that need to be tuned.
We will first start by tuning the default difficulty of our PoW system. That's gonna define an expected time for attackers and clients to succeed.
We are then gonna tune the parameters of the argon2 hash function. That will define the resources that an attacker needs to spend to overwhelm the onion service, the resources that the service needs to spend to verify introduction requests, and the resources that legitimate clients need to spend to get to the onon service.
5.1. PoW Difficulty settings
The difficulty setting of our PoW basically dictates how difficult it should be to get a success in our PoW system. In classic PoW systems, "success" is defined as getting a hash output below the "target". However, since our system is dynamic, we define "success" as an abstract high-effort computation.
Even tho our system is dynamic, we still need default difficulty settings that will define the metagame. The client and attacker can still aim higher or lower, but for UX purposes and for analysis purposes we do need to define some difficulties.
We hence created the table (see [REF_TABLE]) below which shows how much time a legitimate client with a single machine should expect to burn before they get a single success. The x-axis is how many successes we want the attacker to be able to do per second: the more successes we allow the adversary, the more they can overwhelm our introduction queue. The y-axis is how many machines the adversary has in her disposal, ranging from just 5 to 1000.
=============================================================== | Expected Time (in seconds) Per Success For One Machine |=========================================================================== | | | Attacker Succeses 1 5 10 20 30 50 | | per second | | | | 5 5 1 0 0 0 0 | | 50 50 10 5 2 1 1 | | 100 100 20 10 5 3 2 | | Attacker 200 200 40 20 10 6 4 | | Boxes 300 300 60 30 15 10 6 | | 400 400 80 40 20 13 8 | | 500 500 100 50 25 16 10 | | 1000 1000 200 100 50 33 20 | | | ============================================================================
Here is how you can read the table above:
If an adversary has a botnet with 1000 boxes, and we want to limit her to 1 success per second, then a legitimate client with a single box should be expected to spend 1000 seconds getting a single success.
If an adversary has a botnet with 1000 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 200 seconds getting a single success.
If an adversary has a botnet with 500 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 100 seconds getting a single success.
If an adversary has access to 50 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 10 seconds getting a single success.
If an adversary has access to 5 boxes, and we want to limit her to 5 successes per second, then a legitimate client with a single box should be expected to spend 1 seconds getting a single success.
With the above table we can create some profiles for default values of our PoW difficulty. So for example, we can use the last case as the default parameter for Tor Browser, and then create three more profiles for more expensive cases, scaling up to the first case which could be hardest since the client is expected to spend 15 minutes for a single introduction.
{TODO: PARAM_TUNING You can see that this section is completely CPU/memory agnostic, and it does not take into account potential optimizations that can come from GPU/ASICs. This is intentional so that we don't put more variables into this equation right now, but as this proposal moves forward we will need to put more concrete values here.}
This is excellent analysis!
Do we know how many "successes per second" (ie: INTRO2+rend response+nginx) a typically spec'ed HS can serve? That would be a useful stat for comparison. Is 50/second unreasonable to expect to survive, on the typical service side?
Related: At what point do people need onionbalance, typically? And how far does that get you, in req/sec handling on a single machine? On multiple machines?
5.2. Argon2 parameters [ARGON_PARAMS]
We now need to define the secondary argon2 parameters as defined in [REF_ARGON2]. This includes the number of lanes 'h', the memory size 'm', the number of iterations 't'. Section 9 of [REF_ARGON2] recommends an approach of how to tune these parameters.
To tune these parameters we are looking to *minimize* the verification speed of an onion service, while *maximizing* the sparse resources spent by an adversary trying to overwhelm the service using [ATTACK_META].
When it comes to verification speed, to verify a single introduction cell the service needs to do a single argon2 call: so the service will need to do hundreds of those per second as INTRODUCE2 cells arrive. The service will have to do this verification step even for very cheap zero-effort PoW received, so this has to be a cheap procedure so that it doesn't become a DoS vector of each own. Hence each individual argon2 call must be cheap enough to be able to be done comfortably and plentifuly by an onion service with a single host (or horizontally scaled with Onionbalance).
At the same time, the adversary will have to do thousands of these calls if she wants to make high-effort PoW, so it's this assymetry that we are looking to exploit here. Right now, the most expensive resource for adversaries is the RAM size, and that's why we chose argon2 which is memory-hard.
To minmax this game we will need
{TODO: PARAM_TUNING: I've had a hard time minmaxing this game for argon2. Even argon2 invocations with a small memory parameter will take multiple milliseconds to run on my machine, and the parameters recommended in section 8 of the paper all take many hundreds of milliseconds. This is just not practical for our use case, since we want to process hundreds of such PoW per second... I also did not manage to find a benchmark of argon2 calls for different CPU/GPU/FPGA configurations.}
TODO: We should write something similar for RandomX.
- Client behavior [CLIENT_BEHAVIOR]
This proposal introduces a bunch of new ways where a legitimate client can fail to reach the onion service.
Furthermore, there is currently no end-to-end way for the onion service to inform the client that the introduction failed. The INTRO_ACK cell is not end-to-end (it's from the introduction point to the client) and hence it does not allow the service to inform the client that the rendezvous is never gonna occur.
Let's examine a few such cases:
5.1. Timeout issues
Alice can fail to reach the onion service if her introduction request falls off the priority queue, or if the priority queue is so big that the connection times out.
Is building a new introduction circuit sufficient here? Or do we need to build an end-to-end mechanism over the introduction circuit to inform her? {XXX}
How should timeout values change here since the priority queue will cause bigger delays than usual to rendezvous? Can there be some feedback mechanism to inform the client of its queue position or ETA?
Clients could estimate this time based on the published descriptor difficulty (ie: lowest-needed-to-service), and how long such a difficulty takes on their platform. They could record their own history for stats and UX reporting.
5.2. Seed expiration issues
As mentioned in [DESC_POW], the expiration timestamp on the PoW seed can cause issues with clock skewed clients. Furthermore, even not clock skewed clients can encounter TOCTOU-style race conditions here.
How should this be handled? Should we have multiple active seeds at the same time similar to how we have overlapping descriptors and time periods in v3? This would solve the problem but it grows the complexity of the system substantially. {XXX}
5.3. Other descriptor issues
Another race condition here is if the service enables PoW, while a client has a cached descriptor. How will the client notice that PoW is needed? Does it need to fetch a new descriptor? Should there be another feedback mechanism? {XXX}
- Discussion
5.1. UX
This proposal has user facing UX consequences. Here are a few UX approaches with increasing engineering difficulty:
a) Tor Browser needs a "range field" which the user can use to specify how much effort they want to spend in PoW if this ever occurs while they are browsing. The ranges could be from "Easy" to "Difficult", or we could try to estimate time using an average computer. This setting is in the Tor Browser settings and users need to find it.
If clients can estimate based on the difficulty, this could be a notice instead of a config option: "This site will take about X seconds to access, as it is under attack. Please be patient, or give up." There is no need to config anything. You decide to give up on a site-by-site and visit-by-visit basis, depending on how important that site is to you at that time.
b) We start with a default effort setting, and then we use the new onion errors (see #19251) to estimate when an onion service connection has failed because of DoS, and only then we present the user a "range field" which they can set dynamically. Detecting when an onion service connection has failed because of DoS can be hard because of the lack of feedback (see [CLIENT_BEHAVIOR])
c) We start with a default effort setting, and if things fail we automatically try to figure out an effort setting that will work for the user by doing some trial-and-error connections with different effort values. Until the connection succeeds we present a "Service is overwhelmed, please wait" message to the user.
For this proposal to work initially we need at least (a), and then we can start thinking of how far we want to take it.
5.2. Future directions [FUTURE_WORK]
This is just the beginning in DoS defences for Tor and there are various future avenues that we can investigate. Here is a brief summary of these:
"More advanced PoW schemes" -- We could use more advanced memory-hard PoW schemes like MTP-argon2 or Itsuku to make it even harder for adversaries to create successful PoWs. Unfortunately these schemes have much bigger proof sizes, and they won't fit in INTRODUCE1 cells. See #31223 for more details.
"Third-party anonymous credentials" -- We can use anonymous credentials and a third-party token issuance server on the clearnet to issue tokens based on PoW or CAPTCHA and then use those tokens to get access to the service. See [REF_CREDS] for more details.
"PoW + Anonymous Credentials" -- We can make a hybrid of the above ideas where we present a hard puzzle to the user when connecting to the onion service, and if they solve it we then give the user a bunch of anonymous tokens that can be used in the future. This can all happen between the client and the service without a need for a third party.
All of the above approaches are much more complicated than this proposal, and hence we want to start easy before we get into more serious projects.
5.3. Environment
We love the environment! We are concerned of how PoW schemes can waste energy by doing useless hash iterations. Here is a few reasons we still decided to pursue a PoW approach here:
"We are not making things worse" -- DoS attacks are already happening and attackers are already burning energy to carry them out both on the attacker side, on the service side and on the network side. We think that asking legitimate clients to carry out PoW computations is not gonna affect the equation too much, since an attacker right now can very quickly cause the same damage that hundreds of legitimate clients do a whole day.
"We hope to make things better" -- The hope is that proposals like this will make the DoS actors go away and hence the PoW system will not be used. As long as DoS is happening there will be a waste of energy, but if we manage to demotivate them with technical means, the network as a whole will less wasteful. Also see [CATCH22] for a similar argument.
- References
https://password-hashing.net/#argon2[REF_TABLE]: The table is based on the script below plus some manual editing for readability: https://gist.github.com/asn-d6/99a936b0467b0cef88a677baaf0bbd04 [REF_BOTNET]: https://media.kasperskycontenthub.com/wp-content/uploads/sites/43/2009/07/01... [REF_CREDS]: https://lists.torproject.org/pipermail/tor-dev/2020-March/014198.html [REF_TARGET]: https://en.bitcoin.it/wiki/Target _______________________________________________ tor-dev mailing list tor-dev@lists.torproject.org https://lists.torproject.org/cgi-bin/mailman/listinfo/tor-dev