```
Filename: 327-pow-over-intro.txt
Title: A First Take at PoW Over Introduction Circuits
Author: George Kadianakis, Mike Perry, David Goulet, tevador
Created: 2 April 2020
Status: Closed

0. Abstract

  This proposal aims to thwart introduction flooding DoS attacks by introducing
  a dynamic Proof-Of-Work protocol that occurs over introduction circuits.

1. 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 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. For more information on the limitations
  of rate-limiting when defending against DDoS, see [REF_TLS_1].

  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. Related work

  For a similar concept, see the three internet drafts that have been proposed
  for defending against TLS-based DDoS attacks using client puzzles [REF_TLS].

1.2. Threat model [THREAT_MODEL]

1.2.1. 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
  adversary 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 GB 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.

  We hope that this proposal can help us defend against the script-kiddie
  attacker and small botnets. To defend against a large botnet we would need
  more tools at our disposal (see [FUTURE_DESIGNS]).

1.2.2. 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. 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 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.

1.2.3. 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.

2. System Overview

2.1. Tor protocol overview

                                          +----------------------------------+
                                          |          Onion Service           |
   +-------+ 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.

  The system described in this proposal is not meant to be on all the time, and
  it can be entirely disabled for services that do not experience DoS attacks.

  When the subsystem is enabled, suggested effort is continuously adjusted and
  the computational puzzle can be bypassed entirely when the effort reaches
  zero. In these cases, the proof-of-work subsystem can be dormant but still
  provide the necessary parameters for clients to voluntarily provide effort
  in order to get better placement in the priority queue.

  The protocol involves the following major steps:

  1) Service encodes PoW parameters in descriptor [DESC_POW]
  2) Client fetches descriptor and computes PoW [CLIENT_POW]
  3) Client completes PoW and sends results in INTRO1 cell [INTRO1_POW]
  4) Service verifies PoW and queues introduction based on PoW effort
     [SERVICE_VERIFY]
  5) Requests are continuously drained from the queue, highest effort first,
     subject to multiple constraints on speed [HANDLE_QUEUE]

2.2. Proof-of-work overview

2.2.1. Algorithm overview

  For our proof-of-work function we will use the Equi-X scheme by tevador
  [REF_EQUIX].  Equi-X is an asymmetric PoW function based on Equihash<60,3>,
  using HashX as the underlying layer. It features lightning fast verification
  speed, and also aims to minimize the asymmetry between CPU and GPU.
  Furthermore, it's designed for this particular use-case and hence
  cryptocurrency miners are not incentivized to make optimized ASICs for it.

  The overall scheme consists of several layers that provide different pieces
  of this functionality:

  1) At the lowest layers, blake2b and siphash are used as hashing and PRNG
     algorithms that are well suited to common 64-bit CPUs.
  2) A custom hash function family, HashX, randomizes its implementation for
     each new seed value. These functions are tuned to utilize the pipelined
     integer performance on a modern 64-bit CPU. This layer provides the
     strongest ASIC resistance, since a hardware reimplementation would need
     to include a CPU-like pipelined execution unit to keep up.
  3) The Equi-X layer itself builds on HashX and adds an algorithmic puzzle
     that's designed to be strongly asymmetric and to require RAM to solve
     efficiently.
  4) The PoW protocol itself builds on this Equi-X function with a particular
     construction of the challenge input and particular constraints on the
     allowed blake2b hash of the solution. This layer provides a linearly
     adjustable effort that we can verify.
  5) Above the level of individual PoW handshakes, the client and service
     form a closed-loop system that adjusts the effort of future handshakes.

  The Equi-X scheme provides two functions that will be used in this proposal:
      - equix_solve(challenge) which solves a puzzle instance, returning
        a variable number of solutions per invocation depending on the specific
        challenge value.
      - equix_verify(challenge, solution) which verifies a puzzle solution
        quickly. Verification still depends on executing the HashX function,
        but far fewer times than when searching for a solution.

  For the purposes of this proposal, all cryptographic algorithms are assumed
  to produce and consume byte strings, even if internally they operate on
  some other data type like 64-bit words. This is conventionally little endian
  order for blake2b, which contrasts with Tor's typical use of big endian.
  HashX itself is configured with an 8-byte output but its input is a single
  64-bit word of undefined byte order, of which only the low 16 bits are used
  by Equi-X in its solution output. We treat Equi-X solution arrays as byte
  arrays using their packed little endian 16-bit representation.

  We tune Equi-X in section [EQUIX_TUNING].

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
  [PARAM_TUNING] for more details about these guarantees and tradeoffs.

  As a way to improve reachability and UX, the service tries to estimate the
  effort needed for clients to get access at any given time and places it in
  the descriptor. See [EFFORT_ESTIMATION] for more details.

2.2.3. PoW effort

  It's common for proof-of-work systems to define an exponential effort
  function based on a particular number of leading zero bits or equivalent.
  For the benefit of our effort estimation system, it's quite useful if we
  instead have a linear scale. We use the first 32 bits of a hashed version
  of the Equi-X solution as compared to the full 32-bit range.

  Conceptually we could define a function:
         unsigned effort(uint8_t *token)
  which takes as its argument a hashed solution, interprets it as a
  bitstring, and returns the quotient of dividing a bitstring of 1s by it.

  So for example:
         effort(00000001100010101101) = 11111111111111111111
                                          / 00000001100010101101
  or the same in decimal:
         effort(6317) = 1048575 / 6317 = 165.

  In practice we can avoid even having to perform this division, performing
  just one multiply instead to see if a request's claimed effort is supported
  by the smallness of the resulting 32-bit hash prefix. This assumes we send
  the desired effort explicitly as part of each PoW solution. We do want to
  force clients to pick a specific effort before looking for a solution,
  otherwise a client could opportunistically claim a very large effort any
  time a lucky hash prefix comes up. Thus the effort is communicated explicitly
  in our protocol, and it forms part of the concatenated Equi-X challenge.

3. 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 suggested-effort
                    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.

        suggested-effort: An unsigned integer specifying an effort value that
                  clients should aim for when contacting the service. Can be
                  zero to mean that PoW is available but not currently
                  suggested for a first connection attempt. See
                  [EFFORT_ESTIMATION] for more details here.

        expiration-time: A timestamp in "YYYY-MM-DDTHH:MM:SS" format (iso time
                         with no space) after which the above seed expires and
                         is no longer valid as the input for PoW. It's needed
                         so that our replay cache does not grow infinitely. It
                         should be set to RAND_TIME(now+7200, 900) seconds.

   The service should refresh its seed when expiration-time passes. The service
   SHOULD keep its previous seed in memory and accept PoWs using it to avoid
   race-conditions with clients that have an old seed. The service SHOULD avoid
   generating two consequent seeds that have a common 4 bytes prefix. See
   [INTRO1_POW] for more info.

   By RAND_TIME(ts, interval) we mean a time between ts-interval and ts, chosen
   uniformly at random.

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 prepared to receive PoW solutions as part of the introduction
  protocol.

  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.

  The client should then extract the <suggested-effort> field to configure its
  PoW 'target' (see [REF_TARGET]). The client SHOULD NOT accept 'target' values
  that will cause unacceptably long PoW computation.

  The client uses a "personalization string" P equal to the following
  nul-terminated ASCII string: "Tor hs intro v1\0".

  The client looks up `ID`, the current 32-byte blinded public ID
  (KP_hs_blind_id) for the onion service.

  To complete the PoW the client follows the following logic:

      a) Client selects a target effort E, based on <suggested-effort> and past
         connection attempt history.
      b) Client generates a secure random 16-byte nonce N, as the starting
         point for the solution search.
      c) Client derives seed C by decoding 'seed-b64'.
      d) Client calculates S = equix_solve(P || ID || C || N || E)
      e) Client calculates R = ntohl(blake2b_32(P || ID || C || N || E || S))
      f) Client checks if R * E <= UINT32_MAX.
        f1) If yes, success! The client can submit N, E, the first 4 bytes of
        C, and S.
        f2) If no, fail! The client interprets N as a 16-byte little-endian
        integer, increments it by 1 and goes back to step d).

  Note that the blake2b hash includes the output length parameter in its
  initial state vector, so a blake2b_32 is not equivalent to the prefix of a
  blake2b_512. We calculate the 32-bit blake2b specifically, and interpret it
  in network byte order as an unsigned integer.

  At the end of the above procedure, the client should have S as the solution
  of the Equix-X puzzle with N as the nonce, C as the seed. How quickly this
  happens depends solely on the target effort E parameter.

  The algorithm as described is suitable for single-threaded computation.
  Optionally, a client may choose multiple nonces and attempt several solutions
  in parallel on separate CPU cores. The specific choice of nonce is entirely
  up to the client, so parallelization choices like this do not impact the
  network protocol's interoperability at all.

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:

     [02] -- PROOF_OF_WORK

   The EXT_FIELD content format is:

        POW_VERSION    [1 byte]
        POW_NONCE      [16 bytes]
        POW_EFFORT     [4 bytes]
        POW_SEED       [4 bytes]
        POW_SOLUTION   [16 bytes]

   where:

    POW_VERSION is 1 for the protocol specified in this proposal
    POW_NONCE is the nonce 'N' from the section above
    POW_EFFORT is the 32-bit integer effort value, in network byte order
    POW_SEED is the first 4 bytes of the seed used

   This will increase the INTRODUCE1 payload size by 43 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 41 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 enabled on the
   service. If it's not enabled, the extension SHOULD BE ignored. If enabled,
   even if the suggested effort is currently zero, the service follows the
   procedure detailed in this section.

   If the service requires the PROOF_OF_WORK extension but received an
   INTRODUCE1 cell without any embedded proof-of-work, the service SHOULD
   consider this cell as a zero-effort introduction for the purposes of the
   priority queue (see section [INTRO_QUEUE]).

3.4.1. PoW verification [POW_VERIFY]

   To verify the client's proof-of-work the service MUST do the following steps:

      a) Find a valid seed C that starts with POW_SEED. Fail if no such seed
         exists.
      b) Fail if N = POW_NONCE is present in the replay cache
              (see [REPLAY_PROTECTION])
      c) Calculate R = ntohl(blake2b_32(P || ID || C || N || E || S))
      d) Fail if R * E > UINT32_MAX
      e) Fail if equix_verify(P || ID || C || N || E, S) != EQUIX_OK
      f) Put the request in the queue with a priority of E

   If any of these steps fail the service MUST ignore this introduction request
   and abort the protocol.

   In this proposal we call the above steps the "top half" of introduction
   handling. If all the steps of the "top half" have 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 active 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: Design and specify a suitable bloom filter for this purpose.}

3.4.2. The Introduction Queue  [INTRO_QUEUE]

3.4.2.1. Adding introductions to the introduction queue [ADD_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 uses its included
  effort commitment value to place each request into the right position of the
  priority_queue: The bigger the effort, the more priority it gets in the
  queue. If two elements have the same effort, the older one has priority over
  the newer one.

3.4.2.2. Handling introductions from the introduction queue [HANDLE_QUEUE]

  The service should handle introductions by pulling from the introduction
  queue. We call this part of introduction handling the "bottom half" because
  most of the computation happens in this stage. For a description of how we
  expect such a system to work in Tor, see [TOR_SCHEDULER] section.

3.4.3. PoW effort estimation [EFFORT_ESTIMATION]

3.4.3.1. High-level description of the effort estimation process

  The service starts with a default suggested-effort value of 0, which keeps
  the PoW defenses dormant until we notice signs of overload.

  The overall process of determining effort can be thought of as a set of
  multiple coupled feedback loops. Clients perform their own effort
  adjustments via [CLIENT_TIMEOUT] atop a base effort suggested by the service.
  That suggestion incorporates the service's control adjustments atop a base
  effort calculated using a sum of currently-queued client effort.

  Each feedback loop has an opportunity to cover different time scales. Clients
  can make adjustments at every single circuit creation request, whereas
  services are limited by the extra load that frequent updates would place on
  HSDir nodes.

  In the combined client/service system these client-side increases are
  expected to provide the most effective quick response to an emerging DoS
  attack. After early clients increase the effort using [CLIENT_TIMEOUT],
  later clients will benefit from the service detecting this increased queued
  effort and offering a larger suggested_effort.

  Effort increases and decreases both have an intrinsic cost. Increasing effort
  will make the service more expensive to contact, and decreasing effort makes
  new requests likely to become backlogged behind older requests. The steady
  state condition is preferable to either of these side-effects, but ultimately
  it's expected that the control loop always oscillates to some degree.

3.4.3.2. Service-side effort estimation

  Services keep an internal effort estimation which updates on a regular
  periodic timer in response to measurements made on the queueing behavior
  in the previous period. These internal effort changes can optionally trigger
  client-visible suggested_effort changes when the difference is great enough
  to warrant republishing to the HSDir.

  This evaluation and update period is referred to as HS_UPDATE_PERIOD.
  The service side effort estimation takes inspiration from TCP congestion
  control's additive increase / multiplicative decrease approach, but unlike
  a typical AIMD this algorithm is fixed-rate and doesn't update immediately
  in response to events.

  {TODO: HS_UPDATE_PERIOD is hardcoded to 300 (5 minutes) currently, but it
   should be configurable in some way. Is it more appropriate to use the
   service's torrc here or a consensus parameter?}

3.4.3.3. Per-period service state

  During each update period, the service maintains some state:

    1. TOTAL_EFFORT, a sum of all effort values for rendezvous requests that
       were successfully validated and enqueued.

    2. REND_HANDLED, a count of rendezvous requests that were actually
       launched. Requests that made it to dequeueing but were too old to launch
       by then are not included.

    3. HAD_QUEUE, a flag which is set if at any time in the update period we
       saw the priority queue filled with more than a minimum amount of work,
       greater than we would expect to process in approximately 1/4 second
       using the configured dequeue rate.

    4. MAX_TRIMMED_EFFORT, the largest observed single request effort that we
       discarded during the period. Requests are discarded either due to age
       (timeout) or during culling events that discard the bottom half of the
       entire queue when it's too full.

3.4.3.4. Service AIMD conditions

  At the end of each period, the service may decide to increase effort,
  decrease effort, or make no changes, based on these accumulated state values:

    1. If MAX_TRIMMED_EFFORT > our previous internal suggested_effort,
       always INCREASE. Requests that follow our latest advice are being
       dropped.

    2. If the HAD_QUEUE flag was set and the queue still contains at least
       one item with effort >= our previous internal suggested_effort,
       INCREASE. Even if we haven't yet reached the point of dropping requests,
       this signal indicates that the our latest suggestion isn't high enough
       and requests will build up in the queue.

    3. If neither condition (1) or (2) are taking place and the queue is below
       a level we would expect to process in approximately 1/4 second, choose
       to DECREASE.

    4. If none of these conditions match, the suggested effort is unchanged.

  When we INCREASE, the internal suggested_effort is increased to either its
  previous value + 1, or (TOTAL_EFFORT / REND_HANDLED), whichever is larger.

  When we DECREASE, the internal suggested_effort is scaled by 2/3rds.

  Over time, this will continue to decrease our effort suggestion any time the
  service is fully processing its request queue. If the queue stays empty, the
  effort suggestion decreases to zero and clients should no longer submit a
  proof-of-work solution with their first connection attempt.

  It's worth noting that the suggested-effort is not a hard limit to the
  efforts that are accepted by the service, and it's only meant to serve as a
  guideline for clients to reduce the number of unsuccessful requests that get
  to the service. The service still adds requests with lower effort than
  suggested-effort to the priority queue in [ADD_QUEUE].

3.4.3.5. Updating descriptor with new suggested effort

  The service descriptors may be updated for multiple reasons including
  introduction point rotation common to all v3 onion services, the scheduled
  seed rotations described in [DESC_POW], and updates to the effort suggestion.
  Even though the internal effort estimate updates on a regular timer, we avoid
  propagating those changes into the descriptor and the HSDir hosts unless
  there is a significant change.

  If the PoW params otherwise match but the seed has changed by less than 15
  percent, services SHOULD NOT upload a new descriptor.

4. 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.

  From the client's perspective there's no way to attribute this failure to
  the service itself rather than the introduction point, so error accounting
  is performed separately for each introduction-point. Existing mechanisms
  will discard an introduction point that's required too many retries.

4.1. Clients handling timeouts [CLIENT_TIMEOUT]

  Alice can fail to reach the onion service if her introduction request gets
  trimmed off the priority queue in [HANDLE_QUEUE], or if the service does not
  get through its priority queue in time and the connection times out.

  This section presents a heuristic method for the client getting service even
  in such scenarios.

  If the rendezvous request times out, the client SHOULD fetch a new descriptor
  for the service to make sure that it's using the right suggested-effort for
  the PoW and the right PoW seed. If the fetched descriptor includes a new
  suggested effort or seed, it should first retry the request with these
  parameters.

  {TODO: This is not actually implemented yet, but we should do it. How often
     should clients at most try to fetch new descriptors? Determined by a
     consensus parameter? This change will also allow clients to retry
     effectively in cases where the service has just been reconfigured to
     enable PoW defenses.}

  Every time the client retries the connection, it will count these failures
  per-introduction-point. These counts of previous retries are combined with
  the service's suggested_effort when calculating the actual effort to spend
  on any individual request to a service that advertises PoW support, even
  when the currently advertised suggested_effort is zero.

  On each retry, the client modifies its solver effort:

    1. If the effort is below (CLIENT_POW_EFFORT_DOUBLE_UNTIL = 1000)
       it will be doubled.

    2. Otherwise, multiply the effort by (CLIENT_POW_RETRY_MULTIPLIER = 1.5).

    3. Constrain the new effort to be at least
       (CLIENT_MIN_RETRY_POW_EFFORT = 8) and no greater than
       (CLIENT_MAX_POW_EFFORT = 10000)

  {TODO: These hardcoded limits should be replaced by timed limits and/or
      an unlimited solver with robust cancellation. This is issue tor#40787}

5. 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.

5.1.1. Overwhelm PoW verification (aka "Overwhelm top half") [ATTACK_TOP_HALF]

  A basic attack here is the adversary spamming with bogus INTRO cells so that
  the service does not have computing capacity to even verify the
  proof-of-work. This adversary tries to overwhelm the procedure in the
  [POW_VERIFY] section.

  That's why we need the PoW algorithm to have a cheap verification time so
  that this attack is not possible: we tune this PoW parameter in section
  [POW_TUNING_VERIFICATION].

5.1.2. Overwhelm rendezvous capacity (aka "Overwhelm bottom half")
       [ATTACK_BOTTOM_HALF]

  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. This adversary tries to overwhelm the procedure
  in the [HANDLE_QUEUE] section.

  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]).

  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.

5.1.3. Hybrid overwhelm strategy [ATTACK_HYBRID]

  If both the top- and bottom- halves are processed by the same thread, this
  opens up the possibility for a "hybrid" attack. Given the performance figures
  for the bottom half (0.31 ms/req.) and the top half (5.5 ms/req.), the
  attacker can optimally deny service by submitting 91 high-effort requests and
  1520 invalid requests per second. This will completely saturate the main loop
  because:

  0.31*(1520+91) ~ 0.5 sec.
  5.5*91         ~ 0.5 sec.

  This attack only has half the bandwidth requirement of [ATTACK_TOP_HALF] and
  half the compute requirement of [ATTACK_BOTTOM_HALF].

  Alternatively, the attacker can adjust the ratio between invalid and
  high-effort requests depending on their bandwidth and compute capabilities.

5.1.4. Gaming the effort estimation logic [ATTACK_EFFORT]

  Another way to beat this system is for the attacker to game the effort
  estimation logic (see [EFFORT_ESTIMATION]). Essentially, there are two attacks
  that we are trying to avoid:

  - Attacker sets descriptor suggested-effort to a very high value effectively
    making it impossible for most clients to produce a PoW token in a
    reasonable timeframe.
  - Attacker sets descriptor suggested-effort to a very small value so that
    most clients aim for a small value while the attacker comfortably launches
    an [ATTACK_BOTTOM_HALF] using medium effort PoW (see [REF_TEVADOR_1])

5.1.4. Precomputed PoW attack

  The attacker may precompute many valid PoW nonces and submit them all at once
  before the current seed expires, overwhelming the service temporarily even
  using a single computer. The current scheme gives the attackers 4 hours to
  launch this attack since each seed lasts 2 hours and the service caches two
  seeds.

  An attacker with this attack might be aiming to DoS the service for a limited
  amount of time, or to cause an [ATTACK_EFFORT] attack.

6. Parameter tuning [POW_TUNING]

  There are various parameters in this PoW system that need to be tuned:

  We first start by tuning the time it takes to verify a PoW token. We do this
  first because it's fundamental to the performance of onion services and can
  turn into a DoS vector of its own. We will do this tuning in a way that's
  agnostic to the chosen PoW function.

  We will then move towards analyzing the client starting difficulty setting
  for our PoW system. That defines the expected time for clients to succeed in
  our system, and the expected time for attackers to overwhelm our system. Same
  as above we will do this in a way that's agnostic to the chosen PoW function.

  Currently, we have hardcoded the initial client starting difficulty at 8,
  but this may be too low to ramp up quickly to various on and off attack
  patterns. A higher initial difficulty may be needed for these, depending on
  their severity. This section gives us an idea of how large such attacks can
  be.

  Finally, using those two pieces we will tune our PoW function and pick the
  right client starting difficulty setting. At the end of this section we will
  know the resources that an attacker needs to overwhelm the onion service, the
  resources that the service needs to verify introduction requests, and the
  resources that legitimate clients need to get to the onion service.

6.1. PoW verification [POW_TUNING_VERIFICATION]

  Verifying a PoW token is the first thing that a service does when it receives
  an INTRODUCE2 cell and it's detailed in section [POW_VERIFY]. This
  verification happens during the "top half" part of the process. Every
  millisecond spent verifying PoW adds overhead to the already existing "top
  half" part of handling an introduction cell. Hence we should be careful to
  add minimal overhead here so that we don't enable attacks like [ATTACK_TOP_HALF].

  During our performance measurements in [TOR_MEASUREMENTS] we learned that the
  "top half" takes about 0.26 msecs in average, without doing any sort of PoW
  verification. Using that value we compute the following table, that describes
  the number of cells we can queue per second (aka times we can perform the
  "top half" process) for different values of PoW verification time:

      +---------------------+-----------------------+--------------+
      |PoW Verification Time| Total "top half" time | Cells Queued |
      |                     |                       |  per second  |
      |---------------------|-----------------------|--------------|
      |    0     msec       |    0.26      msec     |    3846      |
      |    1     msec       |    1.26      msec     |    793       |
      |    2     msec       |    2.26      msec     |    442       |
      |    3     msec       |    3.26      msec     |    306       |
      |    4     msec       |    4.26      msec     |    234       |
      |    5     msec       |    5.26      msec     |    190       |
      |    6     msec       |    6.26      msec     |    159       |
      |    7     msec       |    7.26      msec     |    137       |
      |    8     msec       |    8.26      msec     |    121       |
      |    9     msec       |    9.26      msec     |    107       |
      |    10    msec       |    10.26     msec     |    97        |
      +---------------------+-----------------------+--------------+

  Here is how you can read the table above:

  - For a PoW function with a 1ms verification time, an attacker needs to send
    793 dummy introduction cells per second to succeed in a [ATTACK_TOP_HALF] attack.

  - For a PoW function with a 2ms verification time, an attacker needs to send
    442 dummy introduction cells per second to succeed in a [ATTACK_TOP_HALF] attack.

  - For a PoW function with a 10ms verification time, an attacker needs to send
    97 dummy introduction cells per second to succeed in a [ATTACK_TOP_HALF] attack.

  Whether an attacker can succeed at that depends on the attacker's resources,
  but also on the network's capacity.

  Our purpose here is to have the smallest PoW verification overhead possible
  that also allows us to achieve all our other goals.

  [Note that the table above is simply the result of a naive multiplication and
  does not take into account all the auxiliary overheads that happen every
  second like the time to invoke the mainloop, the bottom-half processes, or
  pretty much anything other than the "top-half" processing.

  During our measurements the time to handle INTRODUCE2 cells dominates any
  other action time: There might be events that require a long processing time,
  but these are pretty infrequent (like uploading a new HS descriptor) and
  hence over a long time they smooth out. Hence extrapolating the total cells
  queued per second based on a single "top half" time seems like good enough to
  get some initial intuition. That said, the values of "Cells queued per
  second" from the table above, are likely much smaller than displayed above
  because of all the auxiliary overheads.]

6.2. PoW difficulty analysis [POW_DIFFICULTY_ANALYSIS]

  The difficulty setting of our PoW basically dictates how difficult it should
  be to get a success in our PoW system. An attacker who can get many successes
  per second can pull a successful [ATTACK_BOTTOM_HALF] attack against our
  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.

  Our system is dynamic but we still need a starting difficulty setting that
  will be used for bootstrapping the system. The client and attacker can still
  aim higher or lower but for UX purposes and for analysis purposes we do need
  to define a starting difficulty, to minimize retries by clients.

6.2.1. Analysis based on adversary power

  In this section we will try to do an analysis of PoW difficulty without using
  any sort of Tor-related or PoW-related benchmark numbers.

  We 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 starting values of our
  PoW difficulty.

6.2.2. Analysis based on Tor's performance [POW_DIFFICULTY_TOR]

  To go deeper here, we can use the performance measurements from
  [TOR_MEASUREMENTS] to get a more specific intuition on the starting
  difficulty. In particular, we learned that completely handling an
  introduction cell takes 5.55 msecs in average. Using that value, we can
  compute the following table, that describes the number of introduction cells
  we can handle per second for different values of PoW verification:

      +---------------------+-----------------------+--------------+
      |PoW Verification Time| Total time to handle  | Cells handled|
      |                     |   introduction cell   |  per second  |
      |---------------------|-----------------------|--------------|
      |    0      msec      |    5.55        msec   |    180.18    |
      |    1      msec      |    6.55        msec   |    152.67    |
      |    2      msec      |    7.55        msec   |    132.45    |
      |    3      msec      |    8.55        msec   |    116.96    |
      |    4      msec      |    9.55        mesc   |    104.71    |
      |    5      msec      |    10.55       msec   |    94.79     |
      |    6      msec      |    11.55       msec   |    86.58     |
      |    7      msec      |    12.55       msec   |    79.68     |
      |    8      msec      |    13.55       msec   |    73.80     |
      |    9      msec      |    14.55       msec   |    68.73     |
      |    10     msec      |    15.55       msec   |    64.31     |
      +---------------------+-----------------------+--------------+

  Here is how you can read the table above:

  - For a PoW function with a 1ms verification time, an attacker needs to send
    152 high-effort introduction cells per second to succeed in a
    [ATTACK_BOTTOM_HALF] attack.

  - For a PoW function with a 10ms verification time, an attacker needs to send
    64 high-effort introduction cells per second to succeed in a
    [ATTACK_BOTTOM_HALF] attack.

  We can use this table to specify a starting difficulty that won't allow our
  target adversary to succeed in an [ATTACK_BOTTOM_HALF] attack.

  Of course, when it comes to this table, the same disclaimer as in section
  [POW_TUNING_VERIFICATION] is valid. That is, the above table is just a
  theoretical extrapolation and we expect the real values to be much lower
  since they depend on auxiliary processing overheads, and on the network's
  capacity.


7. Discussion

7.1. UX

  This proposal has user facing UX consequences.

  When the client first attempts a pow, it can note how long iterations of the
  hash function take, and then use this to determine an estimation of the
  duration of the PoW. This estimation could be communicated via the control
  port or other mechanism, such that the browser could display how long the
  PoW is expected to take on their device. If the device is a mobile platform,
  and this time estimation is large, it could recommend that the user try from
  a desktop machine.

7.2. Future work [FUTURE_WORK]

7.2.1. Incremental improvements to this proposal

  There are various improvements that can be done in this proposal, and while
  we are trying to keep this v1 version simple, we need to keep the design
  extensible so that we build more features into it. In particular:

  - End-to-end introduction ACKs

    This proposal suffers from various UX issues because there is no end-to-end
    mechanism for an onion service to inform the client about its introduction
    request. If we had end-to-end introduction ACKs many of the problems from
    [CLIENT_BEHAVIOR] would be alleviated. The problem here is that end-to-end
    ACKs require modifications on the introduction point code and a network
    update which is a lengthy process.

  - Multithreading scheduler

    Our scheduler is pretty limited by the fact that Tor has a single-threaded
    design. If we improve our multithreading support we could handle a much
    greater amount of introduction requests per second.

7.2.2. Future designs [FUTURE_DESIGNS]

  This is just the beginning in DoS defences for Tor and there are various
  future designs and schemes 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.

7.3. 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.

8. Acknowledgements

  Thanks a lot to tevador for the various improvements to the proposal and for
  helping us understand and tweak the RandomX scheme.

  Thanks to Solar Designer for the help in understanding the current PoW
  landscape, the various approaches we could take, and teaching us a few neat
  tricks.

Appendix A.  Little-t tor introduction scheduler

  This section describes how we will implement this proposal in the "tor"
  software (little-t tor).

  The following should be read as if tor is an onion service and thus the end
  point of all inbound data.

A.1. The Main Loop [MAIN_LOOP]

  Tor uses libevent for its mainloop. For network I/O operations, a mainloop
  event is used to inform tor if it can read on a certain socket, or a
  connection object in tor.

  From there, this event will empty the connection input buffer (inbuf) by
  extracting and processing a cell at a time. The mainloop is single threaded
  and thus each cell is handled sequentially.

  Processing an INTRODUCE2 cell at the onion service means a series of
  operations (in order):

    1) Unpack cell from inbuf to local buffer.

    2) Decrypt cell (AES operations).

    3) Parse cell header and process it depending on its RELAY_COMMAND.

    4) INTRODUCE2 cell handling which means building a rendezvous circuit:
        i)  Path selection
        ii) Launch circuit to first hop.

    5) Return to mainloop event which essentially means back to step (1).

  Tor will read at most 32 cells out of the inbuf per mainloop round.

A.2. Requirements for PoW

  With this proposal, in order to prioritize cells by the amount of PoW work
  it has done, cells can _not_ be processed sequentially as described above.

  Thus, we need a way to queue a certain number of cells, prioritize them and
  then process some cell(s) from the top of the queue (that is, the cells that
  have done the most PoW effort).

  We thus require a new cell processing flow that is _not_ compatible with
  current tor design. The elements are:

    - Validate PoW and place cells in a priority queue of INTRODUCE2 cells (as
      described in section [INTRO_QUEUE]).

    - Defer "bottom half" INTRO2 cell processing for after cells have been
      queued into the priority queue.

A.3. Proposed scheduler [TOR_SCHEDULER]

  The intuitive way to address the A.2 requirements would be to do this
  simple and naive approach:

    1) Mainloop: Empty inbuf INTRODUCE2 cells into priority queue

    2) Process all cells in pqueue

    3) Goto (1)

  However, we are worried that handling all those cells before returning to the
  mainloop opens possibilities of attack by an adversary since the priority
  queue is not gonna be kept up to date while we process all those cells. This
  means that we might spend lots of time dealing with introductions that don't
  deserve it. See [BOTTOM_HALF_SCHEDULER] for more details.

  We thus propose to split the INTRODUCE2 handling into two different steps:
  "top half" and "bottom half" process, as also mentioned in [POW_VERIFY]
  section above.

A.3.1. Top half and bottom half scheduler

  The top half process is responsible for queuing introductions into the
  priority queue as follows:

    a) Unpack cell from inbuf to local buffer.

    b) Decrypt cell (AES operations).

    c) Parse INTRODUCE2 cell header and validate PoW.

    d) Return to mainloop event which essentially means step (1).

  The top-half basically does all operations of section [MAIN_LOOP] except from (4).

  An then, the bottom-half process is responsible for handling introductions
  and doing rendezvous. To achieve this we introduce a new mainloop event to
  process the priority queue _after_ the top-half event has completed. This new
  event would do these operations sequentially:

    a) Pop INTRODUCE2 cell from priority queue.

    b) Parse and process INTRODUCE2 cell.

    c) End event and yield back to mainloop.

A.3.2. Scheduling the bottom half process [BOTTOM_HALF_SCHEDULER]

  The question now becomes: when should the "bottom half" event get triggered
  from the mainloop?

  We propose that this event is scheduled in when the network I/O event
  queues at least 1 cell into the priority queue. Then, as long as it has a
  cell in the queue, it would re-schedule itself for immediate execution
  meaning at the next mainloop round, it would execute again.

  The idea is to try to empty the queue as fast as it can in order to provide a
  fast response time to an introduction request but always leave a chance for
  more cells to appear between cell processing by yielding back to the
  mainloop. With this we are aiming to always have the most up-to-date version
  of the priority queue when we are completing introductions: this way we are
  prioritizing clients that spent a lot of time and effort completing their PoW.

  If the size of the queue drops to 0, it stops scheduling itself in order to
  not create a busy loop. The network I/O event will re-schedule it in time.

  Notice that the proposed solution will make the service handle 1 single
  introduction request at every main loop event. However, when we do
  performance measurements we might learn that it's preferable to bump the
  number of cells in the future from 1 to N where N <= 32.

A.4 Performance measurements

  This section will detail the performance measurements we've done on tor.git
  for handling an INTRODUCE2 cell and then a discussion on how much more CPU
  time we can add (for PoW validation) before it badly degrades our
  performance.

A.4.1 Tor measurements [TOR_MEASUREMENTS]

  In this section we will derive measurement numbers for the "top half" and
  "bottom half" parts of handling an introduction cell.

  These measurements have been done on tor.git at commit
  80031db32abebaf4d0a91c01db258fcdbd54a471.

  We've measured several set of actions of the INTRODUCE2 cell handling process
  on Intel(R) Xeon(R) CPU E5-2650 v4. Our service was accessed by an array of
  clients that sent introduction requests for a period of 60 seconds.

  1. Full Mainloop Event

     We start by measuring the full time it takes for a mainloop event to
     process an inbuf containing INTRODUCE2 cells. The mainloop event processed
     2.42 cells per invocation on average during our measurements.

     Total measurements: 3279

       Min: 0.30 msec - 1st Q.: 5.47 msec - Median: 5.91 msec
       Mean: 13.43 msec - 3rd Q.: 16.20 msec - Max: 257.95 msec

  2. INTRODUCE2 cell processing (bottom-half)

     We also measured how much time the "bottom half" part of the process
     takes. That's the heavy part of processing an introduction request as seen
     in step (4) of the [MAIN_LOOP] section:

     Total measurements: 7931

       Min: 0.28 msec - 1st Q.: 5.06 msec - Median: 5.33 msec
       Mean: 5.29 msec - 3rd Q.: 5.57 msec - Max: 14.64 msec

  3. Connection data read (top half)

     Now that we have the above pieces, we can use them to measure just the
     "top half" part of the procedure. That's when bytes are taken from the
     connection inbound buffer and parsed into an INTRODUCE2 cell where basic
     validation is done.

     There is an average of 2.42 INTRODUCE2 cells per mainloop event and so we
     divide that by the full mainloop event mean time to get the time for one
     cell. From that we subtract the "bottom half" mean time to get how much
     the "top half" takes:

        => 13.43 / (7931 / 3279) = 5.55
        => 5.55 - 5.29 = 0.26

        Mean: 0.26 msec

  To summarize, during our measurements the average number of INTRODUCE2 cells
  a mainloop event processed is ~2.42 cells (7931 cells for 3279 mainloop
  invocations).

  This means that, taking the mean of mainloop event times, it takes ~5.55msec
  (13.43/2.42) to completely process an INTRODUCE2 cell. Then if we look deeper
  we see that the "top half" of INTRODUCE2 cell processing takes 0.26 msec in
  average, whereas the "bottom half" takes around 5.33 msec.

  The heavyness of the "bottom half" is to be expected since that's where 95%
  of the total work takes place: in particular the rendezvous path selection
  and circuit launch.

A.2. References

    [REF_EQUIX]: https://github.com/tevador/equix
                 https://github.com/tevador/equix/blob/master/devlog.md
    [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/43/2009/07/01121538/ynam_botnets_0907_en.pdf
    [REF_CREDS]: https://lists.torproject.org/pipermail/tor-dev/2020-March/014198.html
    [REF_TARGET]: https://en.bitcoin.it/wiki/Target
    [REF_TLS]: https://www.ietf.org/archive/id/draft-nygren-tls-client-puzzles-02.txt
               https://datatracker.ietf.org/doc/html/draft-nir-tls-puzzles-00.html
               https://tools.ietf.org/html/draft-ietf-ipsecme-ddos-protection-10
    [REF_TLS_1]: https://www.ietf.org/archive/id/draft-nygren-tls-client-puzzles-02.txt
    [REF_TEVADOR_1]: https://lists.torproject.org/pipermail/tor-dev/2020-May/014268.html
    [REF_TEVADOR_2]: https://lists.torproject.org/pipermail/tor-dev/2020-June/014358.html
    [REF_TEVADOR_SIM]: https://github.com/mikeperry-tor/scratchpad/blob/master/tor-pow/effort_sim.py#L57
```
