.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "api/gallery/privacy/networks/plot_02_tor_onion_routing.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_api_gallery_privacy_networks_plot_02_tor_onion_routing.py>`
        to download the full example code or to run this example in your browser via JupyterLite.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_api_gallery_privacy_networks_plot_02_tor_onion_routing.py:


Onion routing in Tor: fast, but exposed end to end (2004)
=========================================================

Tor wraps traffic in layers like a mix-net, through a circuit of three
relays, but forwards each packet at once: no batching, no reordering. That
makes browsing fast enough to use. The design paper states the price: an
adversary that watches both ends of a circuit can match the packet timing
going in to the timing coming out. Delays shift every packet alike, so the
gaps between packets survive the trip.

An adversary running a fraction :math:`f` of the relays sees both ends of
a random circuit with probability about

.. math::

   \Pr[\text{entry and exit malicious}] \approx f^2 .

.. GENERATED FROM PYTHON SOURCE LINES 21-27

.. code-block:: Python

    from random import Random

    import matplotlib.pyplot as plt

    import blockchainkit as bk








.. GENERATED FROM PYTHON SOURCE LINES 28-31

A three-hop circuit
-------------------
The guard knows the user, the exit knows the destination, nobody knows both.

.. GENERATED FROM PYTHON SOURCE LINES 31-40

.. code-block:: Python


    guard, middle, exit_ = (
        bk.privacy.MixNode(name, s) for name, s in (("guard", 7), ("middle", 8), ("exit", 9))
    )
    cell = bk.privacy.wrap(b"GET /", [guard, middle, exit_], "example.org", Random(0))
    for relay in (guard, middle, exit_):
        hop, cell = relay.peel(cell)
        print(f"{relay.name} sends to {hop}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    guard sends to middle
    middle sends to exit
    exit sends to example.org




.. GENERATED FROM PYTHON SOURCE LINES 41-45

Timing correlation
------------------
Fifty users browse at once; the adversary records packet times at
entry and exit and matches flows by their gaps.

.. GENERATED FROM PYTHON SOURCE LINES 45-52

.. code-block:: Python


    observed = bk.privacy.simulate_flows(50, 30, seed=3)
    guesses = bk.privacy.match_flows(observed.entering, observed.leaving)
    correct = sum(g == t for g, t in zip(guesses, observed.truth, strict=True))
    print(f"flows matched correctly: {correct} of 50")
    assert correct == 50





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    flows matched correctly: 50 of 50




.. GENERATED FROM PYTHON SOURCE LINES 53-55

How often both ends are watched
-------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 55-73

.. code-block:: Python


    fractions = [0.05, 0.1, 0.2, 0.3, 0.4]
    rates = [
        bk.privacy.circuit_compromise_rate(100, range(int(f * 100)), 20_000, seed=1) for f in fractions
    ]
    for f, rate in zip(fractions, rates, strict=True):
        assert abs(rate - f * f) < 0.02

    fig, ax = plt.subplots(figsize=(6, 4))
    ax.plot(fractions, rates, marker="o", label="simulated circuits")
    ax.plot(fractions, [f * f for f in fractions], linestyle="--", color="#64748b", label="$f^2$")
    ax.set(xlabel="fraction of relays malicious", ylabel="circuits exposed end to end")
    ax.set_title("Low latency trades away timing protection")
    ax.legend()
    fig.tight_layout()

    plt.show()




.. image-sg:: /api/gallery/privacy/networks/images/sphx_glr_plot_02_tor_onion_routing_001.png
   :alt: Low latency trades away timing protection
   :srcset: /api/gallery/privacy/networks/images/sphx_glr_plot_02_tor_onion_routing_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 74-80

Exercise
--------
Tor clients keep the same entry guard for months. If 10% of relays are
malicious, what is the chance a user's guard is bad, and why is that
better than choosing a fresh entry for every circuit?
A worked solution is in :doc:`/exercises/privacy`.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.227 seconds)


.. _sphx_glr_download_api_gallery_privacy_networks_plot_02_tor_onion_routing.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../../../lite/lab/index.html?path=api/gallery/privacy/networks/plot_02_tor_onion_routing.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_02_tor_onion_routing.ipynb <plot_02_tor_onion_routing.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_02_tor_onion_routing.py <plot_02_tor_onion_routing.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_02_tor_onion_routing.zip <plot_02_tor_onion_routing.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_