tor/doc/HACKING/CircuitPaddingDevelopment.md

Circuit Padding Developer Documentation

This document is written for researchers who wish to prototype and evaluate circuit-level padding defenses in Tor.

Written by Mike Perry and George Kadianakis.

Table of Contents

• 0. Background
• 1. Introduction
  • 1.1. System Overview
  • 1.2. Layering Model
  • 1.3. Computation Model
  • 1.4. Deployment Constraints
• 2. Creating New Padding Machines
  • 2.1. Registering a New Padding Machine
  • 2.2. Machine Activation and Shutdown
• 3. Specifying Padding Machines
  • 3.1. Padding Machine States
  • 3.2. Padding Machine State Transitions
  • 3.3. Specifying Per-State Padding
  • 3.4. Specifying Precise Cell Counts
  • 3.5. Specifying Overhead Limits
• 4. Evaluating Padding Machines
  • 4.1. Pure Simulation
  • 4.2. Testing in Chutney
  • 4.3. Testing in Shadow
  • 4.4. Testing on the Live Network
• 5. Example Padding Machines
  • 5.1. Deployed Circuit Setup Machines
  • 5.2. Adaptive Padding Early
  • 5.3. Sketch of Tamaraw
  • 5.4. Other Padding Machines
• 6. Framework Implementation Details
  • 6.1. Memory Allocation Conventions
  • 6.2. Machine Application Events
  • 6.3. Internal Machine Events
• 7. Future Features and Optimizations
  • 7.1. Load Balancing and Flow Control
  • 7.2. Timing and Queuing Optimizations
  • 7.3. Better Machine Negotiation
  • 7.4. Probabilistic State Transitions
  • 7.5. More Complex Pattern Recognition
• 8. Open Research Problems
  • 8.1. Onion Service Circuit Setup
  • 8.2. Onion Service Fingerprinting
  • 8.3. Open World Fingerprinting
  • 8.4. Protocol Usage Fingerprinting
  • 8.5. Datagram Transport Side Channels
• 9. Must Read Papers

0. Background

Tor supports both connection-level and circuit-level padding, and both systems are live on the network today. The connection-level padding behavior is described in section 2 of padding-spec.txt. The circuit-level padding behavior is described in section 3 of padding-spec.txt.

These two systems are orthogonal and should not be confused. The connection-level padding system is only active while the TLS connection is otherwise idle. Moreover, it regards circuit-level padding as normal data traffic, and hence while the circuit-level padding system is actively padding, the connection-level padding system will not add any additional overhead.

While the currently deployed circuit-level padding behavior is quite simple, it is built on a flexible framework. This framework supports the description of event-driven finite state machine by filling in fields of a simple C structure, and is designed to support any delay-free statistically shaped cover traffic on individual circuits, with cover traffic flowing to and from a node of the implementor's choice (Guard, Middle, Exit, Rendezvous, etc).

This class of system was first proposed in Timing analysis in low-latency mix networks: attacks and defenses by Shmatikov and Wang, and extended for the website traffic fingerprinting domain by Juarez et al. in Toward an Efficient Website Fingerprinting Defense. The framework also supports fixed parameterized probability distributions, as used in APE by Tobias Pulls, and many other features.

This document describes how to use Tor's circuit padding framework to implement and deploy novel delay-free cover traffic defenses.

1. Introduction

The circuit padding framework is the official way to implement padding defenses in Tor. It may be used in combination with application-layer defenses, and/or obfuscation defenses, or on its own.

Its current design should be enough to deploy most defenses without modification, but you can extend it to provide new features as well.

1.1. System Overview

Circuit-level padding can occur between Tor clients and relays at any hop of one of the client's circuits. Both parties need to support the same padding mechanisms for the system to work, and the client must enable it.

We added a padding negotiation relay cell to the Tor protocol that clients use to ask a relay to start padding, as well as a torrc directive for researchers to pin their clients' relay selection to the subset of Tor nodes that implement their custom defenses, to support ethical live network testing and evaluation.

Circuit-level padding is performed by 'padding machines'. A padding machine is a finite state machine. Every state specifies a different form of padding style, or stage of padding, in terms of inter-packet timings and total packet counts.

Padding state machines are specified by filling in fields of a C structure, which specifies the transitions between padding states based on various events, probability distributions of inter-packet delays, and the conditions under which padding machines should be applied to circuits.

This compact C structure representation is designed to function as a microlanguage, which can be compiled down into a bitstring that can be tuned using various optimization methods (such as gradient descent, GAs, or GANs), either in bitstring form or C struct form.

The event driven, self-contained nature of this framework is also designed to make evaluation both expedient and rigorously reproducible.

This document covers the engineering steps to write, test, and deploy a padding machine, as well as how to extend the framework to support new machine features.

If you prefer to learn by example, you may want to skip to either the QuickStart Guide, and/or Section 5 for example machines to get you up and running quickly.

1.2. Layering Model

The circuit padding framework is designed to provide one layer in a layered system of interchangeable components.

The circuit padding framework operates at the Tor circuit layer. It only deals with the inter-cell timings and quantity of cells sent on a circuit. It can insert cells on a circuit in arbitrary patterns, and in response to arbitrary conditions, but it cannot delay cells. It also does not deal with packet sizes, how cells are packed into TLS records, or ways that the Tor protocol might be recognized on the wire.

The problem of differentiating Tor traffic from non-Tor traffic based on TCP/TLS packet sizes, initial handshake patterns, and DPI characteristics is the domain of pluggable transports, which may optionally be used in conjunction with this framework (or without it).

This document focuses primarily on the circuit padding framework's cover traffic features, and will only briefly touch on the potential obfuscation and application layer coupling points of the framework. Explicit layer coupling points can be created by adding either new machine application events or new internal machine events to the circuit padding framework, so that your padding machines can react to events from other layers.

1.3. Computation Model

The circuit padding framework is designed to support succinctly specified defenses that can be tuned through computer-assisted optimization.

We chose to generalize the original Adaptive Padding 2-state design into an event-driven state machine because state machines are the simplest form of sequence recognition devices from automata theory.

Most importantly: this framing allows cover traffic defenses to be modeled as an optimization problem search space, expressed as fields of a C structure (which is simultaneously a compact opaque bitstring as well as a symbolic vector in an abstract feature space). This kind of space is particularly well suited to search by gradient descent, GAs, and GANs.

When performing this optimization search, each padding machine should have a fitness function, which will allow two padding machines to be compared for relative effectiveness. Optimization searches work best if this fitness can be represented as a single number, for example the total amount by which it reduces the Balanced Accuracy of an adversary's classifier, divided by an amount of traffic overhead.

Before you begin the optimization phase for your defense, you should also carefully consider the features and optimizations that we suspect will be useful, and also see if you can come up with any more. You should similarly be sure to restrict your search space to avoid areas of the bitstring/feature vector that you are sure you will not need. For example, some applications may not need the histogram accounting used by Adaptive Padding, but might need to add other forms of pattern recognition to react to sequences that resemble HTTP GET and HTTP POST.

1.4. Other Deployment Constraints

The framework has some limitations that are the result of deliberate choices. We are unlikely to deploy defenses that ignore these limitations.

In particular, we have deliberately not provided any mechanism to delay actual user traffic, even though we are keenly aware that if we were to support additional delay, defenses would be able to have more success with less bandwidth overhead.

In the website traffic fingerprinting domain, provably optimal defenses achieve their bandwidth overhead bounds by ensuring that a non-empty queue is maintained, by rate limiting traffic below the actual throughput of a circuit. For optimal results, this queue must avoid draining to empty, and yet it must also be drained fast enough to avoid tremendous queue overhead in fast Tor relays, which carry hundreds of thousands of circuits simultaneously.

Unfortunately, Tor's end-to-end flow control is not congestion control. Its window sizes are currently fixed. This means there is no signal when queuing occurs, and thus no ability to limit queue size through pushback. This means there is currently no way to do the fine-grained queue management necessary to create such a queue and rate limit traffic effectively enough to keep this queue from draining to empty, without also risking that aggregate queuing would cause out-of-memory conditions on fast relays.

It may be possible to create a congestion control algorithm that can support such fine grained queue management, but this is a deeply unsolved area of research.

Even beyond these major technical hurdles, additional latency is also unappealing to the wider Internet community, for the simple reason that bandwidth continues to increase exponentially where as the speed of light is fixed. Significant engineering effort has been devoted to optimizations that reduce the effect of latency on Internet protocols. To go against this trend would ensure our irrelevance to the wider conversation about traffic analysis defenses for low latency Internet protocols.

On the other hand, through load balancing and circuit multiplexing strategies, we believe it is possible to add significant bandwidth overhead in the form of cover traffic, without significantly impacting end-user performance.

For these reasons, we believe the trade-off should be in favor of adding more cover traffic, rather than imposing queuing memory overhead and queuing delay.

As a last resort for narrowly scoped application domains (such as shaping Tor service-side onion service traffic to look like other websites or different application-layer protocols), delay may be added at the application layer. Any additional cover traffic required by such defenses should still be added at the circuit padding layer using this framework, to provide engineering efficiency through loose layer coupling and component re-use, as well as to provide additional gains against low resolution end-to-end traffic correlation.

Because such delay-based defenses will impact performance significantly more than simply adding cover traffic, they must be optional, and negotiated by only specific application layer endpoints that want them. This will have consequences for anonymity sets and base rates, if such traffic shaping and additional cover traffic is not very carefully constructed.

In terms of acceptable overhead, because Tor onion services currently use less than 1% of the total consumed bandwidth of the Tor network, and because onion services exist to provide higher security as compared to Tor Exit traffic, they are an attractive target for higher-overhead defenses. We encourage researchers to target this use case for defenses that require more overhead, and/or for the deployment of optional negotiated application-layer delays on either the server or the client side.

2. Creating New Padding Machines

This section explains how to use the existing mechanisms in Tor to define a new circuit padding machine. We assume here that you know C, and are at least somewhat familiar with Tor development. For more information on Tor development in general, see the other files in doc/HACKING/ in a recent Tor distribution.

Again, if you prefer to learn by example, you may want to skip to either the QuickStart Guide, and/or Section 5 for example machines to get up and running quickly.

To create a new padding machine, you must:

1. Define your machine using the fields of a heap-allocated circpad_machine_spec_t C structure.

2. Register this object in the global list of available padding machines, using circpad_register_padding_machine().

3. Ensure that your machine is properly negotiated under your desired circuit conditions.

2.1. Registering a New Padding Machine

Again, a circuit padding machine is designed to be specified entirely as a single C structure.

Your machine definitions should go into their own functions in circuitpadding_machines.c. For details on all of the fields involved in specifying a padding machine, see Section 3.

You must register your machine in circpad_machines_init() in circuitpadding.c. To add a new padding machine specification, you must allocate a circpad_machine_spec_t on the heap with tor_malloc_zero(), give it a human readable name string, and a machine number equivalent to the number of machines in the list, and register the structure using circpad_register_padding_machine().

Each machine must have a client instance and a relay instance. Register your client-side machine instance in the origin_padding_machines list, and your relay side machine instance in the relay_padding_machines list. Once you have registered your instance, you do not need to worry about deallocation; this is handled for you automatically.

Both machine lists use registration order to signal machine precedence for a given machine_idx slot on a circuit. This means that machines that are registered last are checked for activation before machines that are registered first. (This reverse precedence ordering allows us to deprecate older machines simply by adding new ones after them.)

2.2. Machine Activation and Shutdown

After a machine has been successfully registered with the framework, it will be instantiated on any client-side circuits that support it. Only client-side circuits may initiate padding machines, but either clients or relays may shut down padding machines.

2.2.1. Machine Application Conditions

The circpad_machine_conditions_t conditions field of your circpad_machine_spec_t machine definition instance controls the conditions under which your machine will be attached and enabled on a Tor circuit, and when it gets shut down.

All of your explicitly specified conditions in circpad_machine_spec_t.conditions must be met for the machine to be applied to a circuit. If any condition ceases to be met, then the machine is shut down. (This is checked on every event that arrives, even if the condition is unrelated to the event.) Another way to look at this is that all specified conditions must evaluate to true for the entire duration that your machine is running. If any are false, your machine does not run (or stops running and shuts down).

In particular, as part of the circpad_machine_conditions_t structure, the circuit padding subsystem gives the developer the option to enable a machine based on:

• The length on the circuit (via the min_hops field).
• The current state of the circuit, such as streams, relay_early, etc. (via the circpad_circuit_state_t state_mask field).
• The purpose (i.e. type) of the circuit (via the circpad_purpose_mask_t purpose_mask field).

This condition mechanism is the preferred way to determine if a machine should apply to a circuit. For information about potentially useful conditions that we have considered but have not yet implemented, see Section 7.3. We will happily accept patches for those conditions, or any for other additional conditions that are needed for your use case.

2.2.2. Detecting and Negotiating Machine Support

When a new machine specification is added to Tor (or removed from Tor), you should bump the Padding subprotocol version in src/core/or/protover.c, add a field to protover_summary_flags_t in or.h, and set this field in memoize_protover_summary() in versions.c. This new field must then be checked in circpad_node_supports_padding() in circuitpadding.c.

Note that this protocol version update and associated support check is not necessary if your experiments will only be using your own relays that support your own padding machines. This can be accomplished by using the MiddleNodes directive; see Section 4 for more information.

If the protocol support check passes for the circuit, then the client sends a RELAY_COMMAND_PADDING_NEGOTIATE cell towards the circpad_machine_spec_t.target_hop relay, and immediately enables the padding machine, and may begin sending padding. (The framework does not wait for the RELAY_COMMAND_PADDING_NEGOTIATED response to begin padding, so that we can switch between machines rapidly.)

2.2.3. Machine Shutdown Mechanisms

Padding machines can be shut down on a circuit in three main ways:

1. During a circpad_machine_event callback, when circpad_machine_spec_t.conditions no longer applies (client side)
2. After a transition to the CIRCPAD_STATE_END, if circpad_machine_spec_t.should_negotiate_end is set (client or relay side)
3. If there is a RELAY_COMMAND_PADDING_NEGOTIATED error response from the relay during negotiation.

Each of these cases causes the originating node to send a relay cell towards the other side, indicating that shutdown has occurred. The client side sends RELAY_COMMAND_PADDING_NEGOTIATE, and the relay side sends RELAY_COMMAND_PADDING_NEGOTIATED.

Because padding from malicious exit nodes can be used to construct active timing-based side channels to malicious guard nodes, the client checks that padding-related cells only come from relays with active padding machines. For this reason, when a client decides to shut down a padding machine, the framework frees the mutable circuit_t.padding_info, but leaves the circuit_t.padding_machine pointer set until the RELAY_COMMAND_PADDING_NEGOTIATED response comes back, to ensure that any remaining in-flight padding packets are recognized a valid. Tor does not yet close circuits due to violation of this property, but the vanguards addon component "bandguard" does.

As an optimization, a client may replace a machine with another, by sending a RELAY_COMMAND_PADDING_NEGOTIATE cell to shut down a machine, and immediately sending a RELAY_COMMAND_PADDING_NEGOTIATE to start a new machine in the same index, without waiting for the response from the first negotiate cell.

Unfortunately, there is a known bug as a consequence of this optimization. If your machine depends on repeated shutdown and restart of the same machine number on the same circuit, please see Bug 30922. Depending on your use case, we may need to fix that bug or help you find a workaround. See also Section 6.1.3 for some more technical details on this mechanism.

3. Specifying Padding Machines

By now, you should understand how to register, negotiate, and control the lifetime of your padding machine, but you still don't know how to make it do anything yet. This section should help you understand how to specify how your machine reacts to events and adds padding to the wire.

If you prefer to learn by example first instead, you may wish to skip to Section 5.

A padding machine is specified by filling in an instance of circpad_machine_spec_t. Instances of this structure specify the precise functionality of a machine: it's what the circuit padding developer is called to write. These instances are created only at startup, and are referenced via const pointers during normal operation.

In this section we will go through the most important elements of this structure.

3.1. Padding Machine States

A padding machine is a finite state machine where each state specifies a different style of padding.

As an example of a simple padding machine, you could have a state machine with the following states: [START] -> [SETUP] -> [HTTP] -> [END] where the [SETUP] state pads in a way that obfuscates the ''circuit setup'' of Tor, and the [HTTP] state pads in a way that emulates a simple HTTP session. Of course, padding machines can be more complicated than that, with dozens of states and non-trivial transitions.

Padding developers encode the machine states in the circpad_machine_spec_t structure. Each machine state is described by a circpad_state_t structure and each such structure specifies the style and amount of padding to be sent, as well as the possible state transitions.

The function circpad_machine_states_init() must be used for allocating and initializing the circpad_machine_spec_t.states array before states and state transitions can be defined, as some of the state object has non-zero default values.

3.2. Padding Machine State Transitions

As described above, padding machines can have multiple states, to support different forms of padding. Machines can transition between states based on events that occur either on the circuit level or on the machine level.

State transitions are specified using the next_state field of the circpad_state_t structure. As a simple example, to transition from state A to state B when event E occurs, you would use the following code: A.next_state[E] = B.

3.2.1. State Transition Events

Here we will go through the various events that can be used to transition between states:

• Circuit-level events
  • CIRCPAD_EVENT_NONPADDING_RECV: A non-padding cell is received
  • CIRCPAD_EVENT_NONPADDING_SENT: A non-adding cell is sent
  • CIRCPAD_EVENT_PADDING_SENT: A padding cell is sent
  • CIRCPAD_EVENT_PADDING_RECV: A padding cell is received
• Machine-level events
  • CIRCPAD_EVENT_INFINITY: Tried to schedule padding using the ''infinity bin''.
  • CIRCPAD_EVENT_BINS_EMPTY: All histogram bins are empty (out of tokens)
  • CIRCPAD_EVENT_LENGTH_COUNT: State has used all its padding capacity (see length_dist below)

3.3. Specifying Per-State Padding

Each state of a padding machine specifies either:

• A padding histogram describing inter-transmission delays between cells; d OR
• A parameterized delay probability distribution for inter-transmission delays between cells.

Either mechanism specifies essentially the minimum inter-transmission time distribution. If non-padding traffic does not get transmitted from this endpoint before the delay value sampled from this distribution expires, a padding packet is sent.

The choice between histograms and probability distributions can be subtle. A rule of thumb is that probability distributions are easy to specify and consume very little memory, but might not be able to describe certain types of complex padding logic. Histograms, in contrast, can support precise packet-count oriented or multimodal delay schemes, and can use token removal logic to reduce overhead and shape the total padding+non-padding inter-packet delay distribution towards an overall target distribution.

We suggest that you start with a probability distribution if possible, and you move to a histogram-based approach only if a probability distribution does not suit your needs.

3.3.1. Padding Probability Distributions

The easiest, most compact way to schedule padding using a machine state is to use a probability distribution that specifies the possible delays. That can be done using the circpad_state_t fields iat_dist, dist_max_sample_usec and dist_added_shift_usec.

The Tor circuit padding framework supports multiple types of probability distributions, and the developer should use the circpad_distribution_t structure to specify them as well as the required parameters.

3.3.2. Padding Histograms

A more advanced way to schedule padding is to use a ''padding histogram''. The main advantages of a histogram are that it allows you to specify distributions that are not easily parameterized in closed form, or require specific packet counts at particular time intervals. Histograms also allow you to make use of an optional traffic minimization and shaping optimization called token removal, which is central to the original Adaptive Padding concept.

If a histogram is used by a state (as opposed to a fixed parameterized distribution), then the developer must use the histogram-related fields of the circpad_state_t structure.

The width of a histogram bin specifies the range of inter-packet delay times, whereas its height specifies the amount of tokens in each bin. To sample a padding delay from a histogram, we first randomly pick a bin (weighted by the amount of tokens in each bin) and then sample a delay from within that bin by picking a uniformly random delay using the width of the bin as the range.

Each histogram also has an ''infinity bin'' as its final bin. If the ''infinity bin'' is chosen, we don't schedule any padding (i.e., we schedule padding with infinite delay). If the developer does not want infinite delay, they should not give any tokens to the ''infinity bin''.

If a token removal strategy is specified (via the circpad_state_t.token_removal field), each time padding is sent using a histogram, the padding machine will remove a token from the appropriate histogram bin whenever this endpoint sends either a padding packet or a non-padding packet. The different removal strategies govern what to do when the bin corresponding to the current inter-packet delay is empty.

Token removal is optional. It is useful if you want to do things like specify a burst should be at least N packets long, and you only want to add padding packets if there are not enough non-padding packets. The cost of doing token removal is additional memory allocations for making per-circuit copies of your histogram that can be modified.

3.4. Specifying Precise Cell Counts

Padding machines should be able to specify the exact amount of padding they send. For histogram-based machines this can be done using a specific amount of tokens, but another (and perhaps easier) way to do this, is to use the length_dist field of the circpad_state_t structure.

The length_dist field is basically a probability distribution similar to the padding probability distributions, which applies to a specific machine state and specifies the amount of padding we are willing to send during that state. This value gets sampled when we transition to that state (TODO document this in the code).

3.5. Specifying Overhead Limits

Separately from the length counts, it is possible to rate limit the overhead percentage of padding at both the global level across all machines, and on a per-machine basis.

At the global level, the overhead percentage of all circuit padding machines as compared to total traffic can be limited through the Tor consensus parameter circpad_global_max_padding_pct. This overhead is defined as the percentage of padding cells sent out of the sum of non padding and padding cells sent, and is applied only after at least circpad_global_allowed_cells padding cells are sent by that relay or client (to allow for small bursts of pure padding on otherwise idle or freshly restarted relays). When both of these limits are hit by a relay or client, no further padding cells will be sent, until sufficient non-padding traffic is sent to cause the percentage of padding traffic to fall back below the threshold.

Additionally, each individual padding machine can rate limit itself by filling in the fields circpad_machine_spec_t.max_padding_percent and circpad_machine_spec_t.allowed_padding_count, which behave identically to the consensus parameters, but only apply to that specific machine.

4. Evaluating Padding Machines

One of the goals of the circuit padding framework is to provide improved evaluation and scientific reproducibility for lower cost. This includes both the choice of the compact C structure representation (which has an easy-to-produce bitstring representation for optimization by gradient descent, GAs, or GANs), as well as rapid prototyping and evaluation.

So far, whenever evaluation cost has been a barrier, each research group has developed their own ad-hoc packet-level simulators of various padding mechanisms for evaluating website fingerprinting attacks and defenses. The process typically involves doing a crawl of Alexa top sites over Tor, and recording the Tor cell count and timing information for each page in the trace. These traces are then fed to simulations of defenses, which output modified trace files.

Because no standardized simulation and evaluation mechanism exists, it is often hard to tell if independent implementations of various attacks and defenses are in fact true-to-form or even properly calibrated for direct comparison, and discrepancies in results across the literature suggests this is not always so.

Our preferred outcome with this framework is that machines are tuned and optimized on a tracing simulator, but that the final results come from an actual live network test of the defense. The traces from this final crawl should be preserved as artifacts to be run on the simulator and reproduced on the live network by future papers, ideally in journal venues that have an artifact preservation policy.

4.1. Pure Simulation

When doing initial tuning of padding machines, especially in adversarial settings, variations of a padding machine defense may need to be applied to network activity hundreds or even millions of times. The wall-clock time required to do this kind of tuning using live testing or even Shadow network emulation may often be prohibitive.

To help address this, and to better standardize results, Tobias Pulls has implemented a circpad machine trace simulator, which uses Tor's unit test framework to simulate applying padding machines to circuit packet traces via a combination of Tor patches and python scripts. This simulator can be used to record traces from clients, Guards, Middles, Exits, and any other hop in the path, only for circuits that are run by the researcher. This makes it possible to safely record baseline traces and ultimately even mount passive attacks on the live network, without impacting or recording any normal user traffic.

In this way, a live crawl of the Alexa top sites could be performed once, to produce a standard "undefended" corpus. Padding machines can be then quickly evaluated and tuned on these simulated traces in a standardized way, and then the results can then be reproduced on the live Tor network with the machines running on your own relays.

Please be mindful of the Limitations section of the simulator documentation, however, to ensure that you are aware of the edge cases and timing approximations that are introduced by this approach.

4.2. Testing in Chutney

The Tor Project provides a tool called Chutney which makes it very easy to setup private Tor networks. While getting it work for the first time might take you some time of doc reading, the final result is well worth it for the following reasons:

• You control all the relays and hence you have greater control and debugging capabilities.
• You control all the relays and hence you can toggle padding support on/off at will.
• You don't need to be cautious about overhead or damaging the real Tor network during testing.
• You don't even need to be online; you can do all your testing offline over localhost.

A final word of warning here is that since Chutney runs over localhost, the packet latencies and delays are completely different from the real Tor network, so if your padding machines rely on real network timings you will get different results on Chutney. You can work around this by using a different set of delays if Chutney is used, or by moving your padding machines to the real network when you want to do latency-related testing.

4.3. Testing in Shadow

Shadow is an environment for running entire Tor network simulations, similar to Chutney, but designed to be both more memory efficient, as well as provide an accurate Tor network topology and latency model.

While Shadow is significantly more memory efficient than Chutney, and can make use of extremely accurate Tor network capacity and latency models, it will not be as fast or efficient as the circpad trace simulator, if you need to do many many iterations of an experiment to tune your defense.

4.4. Testing on the Live Network

Live network testing is the gold standard for verifying that any attack or defense is behaving as expected, to minimize the influence of simplifying assumptions.

However, it is not ethical, or necessarily possible, to run high-resolution traffic analysis attacks on the entire Tor network. But it is both ethical and possible to run small scale experiments that target only your own clients, who will only use your own Tor relays that support your new padding machines.

We provide the MiddleNodes torrc directive to enable this, which will allow you to specify the fingerprints and/or IP netmasks of relays to be used in the second hop position. Options to restrict other hops also exist, if your padding system is padding to a different hop. The HSLayer2Nodes option overrides the MiddleNodes option for onion service circuits, if both are set. (The vanguards addon will set HSLayer2Nodes.)

When you run your own clients, and use MiddleNodes to restrict your clients to use your relays, you can perform live network evaluations of a defense applied to whatever traffic crawl or activity your clients do.

5. Example Padding Machines

5.1. Deployed Circuit Setup Machines

Tor currently has two padding machines enabled by default, which aim to hide certain features of the client-side onion service circuit setup protocol. For more details on their precise goal and function, please see proposal 302 . In this section we will go over the code of those machines to clarify some of the engineering parts:

5.1.1. Overview

The codebase of proposal 302 can be found in circuitpadding_machines.c and specifies four padding machines:

• The client-side introduction circuit machine.
• The relay-side introduction circuit machine.
• The client-side rendezvous circuit machine
• The relay-side rendezvous circuit machine.

Each of those machines has its own setup function, and they are all initialized by the circuit padding framework. To understand more about them, please carefully read the individual setup function for each machine which are fairly well documented. Each function goes through the following steps:

• Machine initialization
  • Give it a name
  • Specify which hop the padding should go to
  • Specify whether it should be client-side or relay-side.
• Specify for which types of circuits the machine should apply
• Specify whether the circuits should be kept alive until the machine finishes padding.
• Sets padding limits to avoid too much overhead in case of bugs or errors.
• Setup machine states
  • Specify state transitions.
• Finally register the machine to the global machine list

5.2. Adaptive Padding Early

Adaptive Padding Early is a variant of Adaptive Padding/WTF-PAD that does not use histograms or token removal to shift padding distributions, but instead uses fixed parameterized distributions to specify inter-packet timing thresholds for burst and gap inter-packet delays.

Tobias Pulls's QuickStart Guide describes how to get this machine up and running, and has links to branches with a working implementation.

5.3. Sketch of Tamaraw

The Tamaraw defense paper is the only defense to date that provides a proof of optimality for the finite-length website traffic fingerprinting domain. These bounds assume that a defense is able to perform a full, arbitrary transform of a trace that is under a fixed number of packets in length.

The key insight to understand Tamaraw's optimality is that it achieves one such optimal transform by delaying traffic below a circuit's throughput. By doing this, it creates a queue that is rarely empty, allowing it to produce a provably optimal transform with minimal overhead. As Section 1.4 explains, this queue cannot be maintained on the live Tor network without risk of out-of-memory conditions at relays.

However, if the queue is not maintained in the Tor network, but instead by the application layer, it could be deployed by websites that opt in to using it.

In this case, the application layer component would do optional constant rate shaping, negotiated between a web browser and a website. The Circuit Padding Framework can then easily fill in any missing gaps of cover traffic packets, and also ensure that only a fixed length number of packets are sent in total.

However, for such a defense to be safe, additional care must be taken to ensure that the resulting traffic pattern still has a large anonymity/confusion set with other traces on the live network.

Accomplishing this is an unsolved problem.

5.4. Other Padding Machines

Our partners in this project at RIT have produced a couple prototypes, based on their published research designs REB and RBB.

As their writeup explains, because RBB uses delay, the circuit padding machine that they made is a no-delay version.

They also ran into an issue with the 0-delay timing workaround for bug 31653. Keep an eye on that bug for updates with improved workarounds/fixes.

Their code is available on github.

6. Framework Implementation Details

If you need to add additional events, conditions, or other features to the circuit padding framework, then this section is for you.

6.1. Memory Allocation Conventions

If the existing circuit padding features are sufficient for your needs, then you do not need to worry about memory management or pointer lifespans. The circuit padding framework should take care of this for you automatically.

However, if you need to add new padding machine features to support your padding machines, then it will be helpful to understand how circuits correspond to the global machine definitions, and how mutable padding machine memory is managed.

6.1.1. Circuits and Padding Machines

In Tor, the circuit_t structure is the superclass structure for circuit-related state that is used on both clients and relays. On clients, the actual datatype of the object pointed to by circuit_t * is the subclass structure origin_circuit_t. The macros CIRCUIT_IS_ORIGIN() and TO_ORIGIN_CIRCUIT() are used to determine if a circuit is a client-side (origin) circuit and to cast the pointer safely to origin_circuit_t *.

Because circuit padding machines can be present at both clients and relays, the circuit padding fields are stored in the circuit_t * superclass structure. Notice that there are actually two sets of circuit padding fields: a const circpad_machine_spec_t * array, and a circpad_machine_runtime_t * array. Each of these arrays holds at most two elements, as there can be at most two padding machines on each circuit.

The const circpad_machine_spec_t * points to a globally allocated machine specification. These machine specifications are allocated and set up during Tor program startup, in circpad_machines_init() in circuitpadding.c. Because the machine specification object is shared by all circuits, it must not be modified or freed until program exit (by circpad_machines_free()). The const qualifier should enforce this at compile time.

The circpad_machine_runtime_t * array member points to the mutable runtime information for machine specification at that same array index. This runtime structure keeps track of the current machine state, packet counts, and other information that must be updated as the machine operates. When a padding machine is successfully negotiated circpad_setup_machine_on_circ() allocates the associated runtime information.

6.1.2. Histogram Management

If a circpad_state_t of a machine specifies a token_removal strategy other than CIRCPAD_TOKEN_REMOVAL_NONE, then every time there is a state transition into this state, circpad_machine_setup_tokens() will copy the read-only circpad_state_t.histogram array into a mutable version at circpad_machine_runtime_t.histogram. This mutable copy is used to decrement the histogram bin accounts as packets are sent, as per the specified token removal strategy.

When the state machine transitions out of this state, the mutable histogram copy is freed by this same circpad_machine_setup_tokens() function.

6.1.3. Deallocation and Shutdown

As an optimization, padding machines can be swapped in and out by the client without waiting a full round trip for the relay machine to shut down.

Internally, this is accomplished by immediately freeing the heap-allocated circuit_t.padding_info field corresponding to that machine, but still preserving the circuit_t.padding_machine pointer to the global padding machine specification until the response comes back from the relay. Once the response comes back, that circuit_t.padding_machine pointer is set to NULL, if the response machine number matches the current machine present.

Because of this partial shutdown condition, we have two macros for iterating over machines. FOR_EACH_ACTIVE_CIRCUIT_MACHINE_BEGIN() is used to iterate over machines that have both a circuit_t.padding_info slot and a circuit_t.padding_machine slot occupied. FOR_EACH_CIRCUIT_MACHINE_BEGIN() is used when we need to iterate over all machines that are either active or are simply waiting for a response to a shutdown request.

If the machine is replaced instead of just shut down, then the client frees the circuit_t.padding_info, and then sets the circuit_t.padding_machine and circuit_t.padding_info fields for this next machine immediately. This is done in circpad_add_matching_machines(). In this case, since the new machine should have a different machine number, the shut down response from the relay is silently discarded, since it will not match the new machine number.

If this sequence of machine teardown and spin-up happens rapidly enough for the same machine number (as opposed to different machines), then a race condition can happen. This is known bug #30992.

When the relay side decides to shut down a machine, it sends a RELAY_COMMAND_PADDING_NEGOTIATED towards the client. If this cell matches the current machine number on the client, that machine is torn down, by freeing the circuit_t.padding_info slot and immediately setting circuit_t.padding_machine slot to NULL.

Additionally, if Tor decides to close a circuit forcibly due to error before the padding machine is shut down, then circuit_t.padding_info is still properly freed by the call to circpad_circuit_free_all_machineinfos() in circuit_free_().

6.2. Machine Application Events

The framework checks client-side origin circuits to see if padding machines should be activated or terminated during specific event callbacks in circuitpadding.c. We list these event callbacks here only for reference. You should not modify any of these callbacks to get your machine to run; instead, you should use the circpad_machine_spec_t.conditions field.

However, you may add new event callbacks if you need other activation events, for example to provide obfuscation-layer or application-layer signaling. Any new event callbacks should behave exactly like the existing callbacks.

During each of these event callbacks, the framework checks to see if any current running padding machines have conditions that no longer apply as a result of the event, and shuts those machines down. Then, it checks to see if any new padding machines should be activated as a result of the event, based on their circuit application conditions. Remember: Machines are checked in reverse order in the machine list. This means that later, more recently added machines take precedence over older, earlier entries in each list.

Both of these checks are performed using the machine application conditions that you specify in your machine's circpad_machine_spec_t.conditions field.

The machine application event callbacks are prefixed by circpad_machine_event_ by convention in circuitpadding.c. As of this writing, these callbacks are:

• circpad_machine_event_circ_added_hop(): Called whenever a new hop is added to a circuit.
• circpad_machine_event_circ_built(): Called when a circuit has completed construction and is opened.
• circpad_machine_event_circ_purpose_changed(): Called when a circuit changes purpose.
• circpad_machine_event_circ_has_no_relay_early(): Called when a circuit runs out of RELAY_EARLY cells.
• circpad_machine_event_circ_has_streams(): Called when a circuit gets a stream attached.
• circpad_machine_event_circ_has_no_streams(): Called when the last stream is detached from a circuit.

6.3. Internal Machine Events

To provide for some additional capabilities beyond simple finite state machine behavior, the circuit padding machines also have internal events that they emit to themselves when packet count length limits are hit, when the Infinity bin is sampled, and when the histogram bins are emptied of all tokens.

These events are implemented as circpad_internal_event_* functions in circuitpadding.c, which are called from various areas that determine when the events should occur.

While the conditions that trigger these internal events to be called may be complex, they are processed by the state machine definitions in a nearly identical manner as the cell processing events, with the exception that they are sent to the current machine only, rather than all machines on the circuit.

7. Future Features and Optimizations

While implementing the circuit padding framework, our goal was to deploy a system that obscured client-side onion service circuit setup and supported deployment of WTF-PAD and/or APE. Along the way, we noticed several features that might prove useful to others, but were not essential to implement immediately. We do not have immediate plans to implement these ideas, but we would gladly accept patches that do so.

The following list gives an overview of these improvements, but as this document ages, it may become stale. The canonical list of improvements that researchers may find useful is labeled in our bugtracker with Padding Research, and the list of improvements that are known to be necessary for some research areas are labeled with Padding Research Requires.

Please consult those lists for the latest status of these issues. Note that not all fixes will be backported to all Tor versions, so be mindful of which Tor releases receive which fixes as you conduct your experiments.

7.1. Load Balancing and Flow Control

Fortunately, non-Exit bandwidth is already plentiful and exceeds the Exit capacity, and we anticipate that if we inform our relay operator community of the need for non-Exit bandwidth to satisfy padding overhead requirements, they will be able to provide that with relative ease.

Unfortunately, padding machines that have large quantities of overhead will require changes to our load balancing system to account for this overhead. The necessary changes are documented in Proposal 265.

Additionally, padding cells are not currently subjected to flow control. For high amounts of padding, we may want to change this. See ticket 31782 for details.

7.2. Timing and Queuing Optimizations

The circuitpadding framework has some timing related issues that may impact results. If high-resolution timestamps are fed to opaque deep learning trainers, those training models may end up able to differentiate padding traffic from non-padding traffic due to these timing bugs.

The circuit padding cell event callbacks come from post-decryption points in the cell processing codepath, and from the pre-queue points in the cell send codepath. This means that our cell events will not reflect the actual time when packets are read or sent on the wire. This is much worse in the send direction, as the circuitmux queue, channel outbuf, and kernel TCP buffer will impose significant additional delay between when we currently report that a packet was sent, and when it actually hits the wire.

Ticket 29494 has a more detailed description of this problem, and an experimental branch that changes the cell event callback locations to be from circuitmux post-queue, which with KIST, should be an accurate reflection of when they are actually sent on the wire.

If your padding machine and problem space depends on very accurate notions of relay-side packet timing, please try that branch and let us know on the ticket if you need any further assistance fixing it up.

Additionally, with these changes, it will be possible to provide further overhead reducing optimizations by letting machines specify flags to indicate that padding should not be sent if there are any cells pending in the cell queue, for doing things like extending cell bursts more accurately and with less overhead.

However, even if we solve the queuing issues, Tor's current timers are not as precise as some padding designs may require. We will still have issues of timing precision to solve. Ticket 31653 describes an issue the circuit padding system has with sending 0-delay padding cells, and ticket 32670 describes a libevent timer accuracy issue, which causes callbacks to vary up to 10ms from their scheduled time, even in absence of load.

All of these issues strongly suggest that you either truncate the resolution of any timers you feed to your classifier, or that you omit timestamps entirely from the classification problem until these issues are addressed.

7.3. Better Machine Negotiation

Circuit padding is applied to circuits through machine conditions.

The following machine conditions may be useful for some use cases, but have not been implemented yet:

• Exit Policy-based Stream Conditions
• Probability to apply machine/Cointoss condition
• Probability distributions for launching new padding circuit(s)
• More flexible purpose handling

Additionally, the following features may help to obscure that padding is being negotiated, and/or streamline that negotiation process:

• Always send negotiation cell on all circuits
• Better shutdown handling
• Preference-ordered negotiation menu

7.4. Probabilistic State Transitions

Right now, the state machine transitions are fully deterministic. However, one could imagine a state machine that uses probabilistic transitions between states to simulate a random walk or Hidden Markov Model traversal across several pages.

The simplest way to implement this is to make the circpad_state_t.next_state array into an array of structs that have a next state field, and a probability to transition to that state.

If you need this feature, please see ticket 31787 for more details.

7.5. More Complex Pattern Recognition

State machines are extremely efficient sequence recognition devices. But they are not great pattern recognition devices. This is one of the reasons why Adaptive Padding used state machines in combination with histograms, to model the target distribution of interpacket delays for transmitted packets.

However, there currently is no such optimization for reaction to patterns of received traffic. There may also be cases where defenses must react to more complex patterns of sent traffic than can be expressed by our current histogram and length count events.

For example: if you wish your machine to react to a certain count of incoming cells in a row, right now you have to have a state for each cell, and use the infinity bin to timeout of the sequence in each state. We could make this more compact if each state had an arrival cell counter and inter-cell timeout. Or we could make more complex mechanisms to recognize certain patterns of arrival traffic in a state.

The best way to build recognition primitives like this into the framework is to add additional Internal Machine Events for the pattern in question.

As another simple example, a useful additional event might be to transition whenever any of your histogram bins are empty, rather than all of them. To do this, you would add CIRCPAD_EVENT_ANY_BIN_EMPTY to the enum circpad_event_t in circuitpadding.h. You would then create a function circuitpadding_internal_event_any_bin_empty(), which would work just like circuitpadding_internal_event_bin_empty(), and also be called from check_machine_token_supply() in circuitpadding.c but with the check for each bin being zero instead of the total. With this change, new machines could react to this new event in the same way as any other.

If you have any other ideas that may be useful, please comment on ticket 32680.

8. Open Research Problems

8.1. Onion Service Circuit Setup

Our circuit setup padding does not address timing-based features, only packet counts. Deep learning can probably see this.

However, before going too deep down the timing rabithole, we may need to make some improvements to Tor. Please comment on those tickets if you need this.

8.2. Onion Service Fingerprinting

We have done nothing to obscure the service side of onion service circuit setup. Because service-side onion services will have the reverse traffic byte counts as normal clients, they will likely need some kind of hybrid application layer traffic shaping, in addition to simple circuit setup obfuscation.

Fingerprinting in combination with vanguards ia also an open issue.

8.3. Open World Fingerprinting

Similarly, Open World/clearweb website fingerprinting defenses remain an unsolved problem from the practicality point of view. The original WTF-PAD defense was never tuned, and it is showing accuracy issues against deep learning attacks.

8.4. Protocol Usage Fingerprinting

Traffic Fingerprinting to determine the protocol in use by a client has not been studied, either from the attack or defense point of view.

8.5. Datagram Transport Side Channels

Padding can reduce the accuracy of dropped-cell side channels in such transports, but we don't know how to measure this.

9. Must Read Papers

These are by far the most important papers in the space, to date:

• Tamaraw
• Bayes, Not Naive
• Anonymity Trilemma
• WTF-PAD

Except for WTF-PAD, these papers were selected because they point towards optimality bounds that can be benchmarked against.

We cite them even though we are skeptical that provably optimal defenses can be constructed, at least not without trivial or impractical transforms (such as those that can be created with unbounded queue capacity, or stored knowledge of traces for every possible HTTP trace on the Internet).

We also are not demanding an optimality or security proof for every defense.

Instead, we cite the above as benchmarks. We believe the space, especially the open-world case, to be more akin to an optimization problem, where a WTF-PAD-like defense must be tuned through an optimizer to produce results comparable to provably optimal but practically unrealizable defenses, through rigorous adversarial evaluation.

A. Acknowledgments

This research was supported in part by NSF grants CNS-1619454 and CNS-1526306.