Guidelines for Performing Safe Measurement on the Internet
draft-irtf-pearg-safe-internet-measurement-07

Network Working Group                                    I. R. Learmonth
Internet-Draft                                                    HamBSD
Intended status: Informational                                 G. Grover
Expires: 29 September 2023               Centre for Internet and Society
                                                               M. Knodel
                                     Center for Democracy and Technology
                                                           28 March 2023

       Guidelines for Performing Safe Measurement on the Internet
             draft-irtf-pearg-safe-internet-measurement-07

Abstract

   Internet measurement is important to researchers from industry,
   academia and civil society.  While measurement can give insight into
   the functioning and usage of the Internet, it can present risks to
   user privacy.  This document describes briefly those risks and
   proposes guidelines for ensuring that internet measurements can be
   carried out safely, with examples.

Note

   This document is a draft.  It is not an IETF product.  It does not
   propose a standard.  Comments are solicited and should be addressed
   to the research group's mailing list at pearg@irtf.org and/or the
   author(s).

   The sources for this draft are at:

   https://github.com/irl/draft-safe-internet-measurement

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on 29 September 2023.

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Copyright Notice

   Copyright (c) 2023 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
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   Please review these documents carefully, as they describe your rights
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Scope of this document  . . . . . . . . . . . . . . . . .   3
     1.2.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   3
     1.3.  User Impact from Measurement Studies  . . . . . . . . . .   4
   2.  Safe Internet Measurement Guidelines  . . . . . . . . . . . .   5
     2.1.  Obtain consent  . . . . . . . . . . . . . . . . . . . . .   5
       2.1.1.  Informed Consent  . . . . . . . . . . . . . . . . . .   5
       2.1.2.  Proxy Consent . . . . . . . . . . . . . . . . . . . .   6
       2.1.3.  Implied Consent . . . . . . . . . . . . . . . . . . .   6
     2.2.  Isolate risk with a dedicated testbed . . . . . . . . . .   7
     2.3.  Be respectful of others' infrastructure . . . . . . . . .   7
     2.4.  Maintain a "Do Not Scan" list . . . . . . . . . . . . . .   8
     2.5.  Minimize data . . . . . . . . . . . . . . . . . . . . . .   8
       2.5.1.  Discard it  . . . . . . . . . . . . . . . . . . . . .   9
       2.5.2.  Mask it . . . . . . . . . . . . . . . . . . . . . . .   9
       2.5.3.  Aggregate it  . . . . . . . . . . . . . . . . . . . .   9
     2.6.  Reduce accuracy . . . . . . . . . . . . . . . . . . . . .   9
     2.7.  Analyze Risk  . . . . . . . . . . . . . . . . . . . . . .  10
   3.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   5.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  10
   6.  Informative References  . . . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

1.  Introduction

   Performing research using the Internet, as opposed to an isolated
   testbed or simulation platform, means that experiments co-exist in a
   space with other services and end users.  This document outlines
   guidelines for academic, industry and civil society researchers who
   might use the Internet as part of scientific experimentation to
   mitigate risks to the safety of other users.

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1.1.  Scope of this document

   These are guidelines for how to measure the Internet safely.  When
   performing research on a platform shared with live traffic from other
   users, that research is considered safe if and only if other users
   are protected from or unlikely to experience danger, risk, or injury,
   now or in the future, due to the research.

   Following the guidelines contained within this document is not a
   substitute for institutional ethics review processes, although these
   guidelines could help to inform that process.  It is particularly
   important for the growing area of research that includes Internet
   measurement to better equip review boards to evaluate Internet
   measurement methods[SIGCOMM], and we hope that this document is part
   of that larger effort.

   Similarly, these guidelines are not legal advice and local laws must
   also be considered before starting any experiment that could have
   adverse impacts on user safety.

   The scope of this document is restricted to guidelines that mitigate
   exposure to risks to Internet user safety when measuring properties
   of the Internet: the network, its constiuent hosts and links, or its
   users traffic.

   For the purpose of this document, an Internet user is an individual
   or organisation whose data is used in communications over the
   Internet, most broadly, and those who use the Internet to communicate
   or maintain Internet infrastructure.

1.2.  Terminology

   Threat model: A threat is a potential for a security violation, which
   exists when there is a circumstance, capability, action, or event
   that could breach security and cause harm [RFC4949].  Every Internet
   measurement study has the potential to subject Internet users to
   threat actions or attacks.

   Surveillance: An attack whereby an Internet user's information is
   collected.  This type of attack covers not only data but also
   metadata.

   Inadequate protection of collected data: An attack where data, either
   in transit or at rest, is not adequately protected from disclosure.
   Failure to adequately protect data to the expectations of the user is
   an attack even if it does not lead to another party gaining access to
   the data.

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   Traffic generation: An attack whereby undue traffic is generated to
   traverse the Internet.

   Traffic modification: An attack whereby on-path Internet traffic is
   unconsentually modified.

   Measurement studies: Internet measurement studies can be broadly
   categorized into two groups: active measurements and passive
   measurements.  The type of measurement is not truly binary and many
   studies will include both active and passive components.

   Active measurement: Active measurements generate or modify traffic.

   Passive measurement: Passive measurements use surveillance of
   existing traffic.

   On/off-path: An attack that is on-path happens on the network.  Off-
   path attacks can happen as side-channel, end-point or other attacks
   or data compromises.

   One-/two-ended:

1.3.  User Impact from Measurement Studies

   Any conceivable Internet measurement study might be considered an
   attack on an Internet user's safety.  The measurement of generated
   traffic may also lead to insights into other users' traffic
   indirectly as well.

   It is always necessary to consider the best approach to mitigate the
   impact of measurements, and to balance the risks of measurements
   against the benefits to impacted users, which we get to in the
   following section.  But first, we describe the consequences of
   attacks that are made possible by measurement.

   Breach of privacy: In the context of data collection.  This impact
   also covers the case of an Internet user's data being shared beyond
   that which a user had given consent for.

   Impersonation: An attack where a user is impersonated during a
   measurement.

   Legal: Users and service providers are bound by a wide range of
   policies from terms of service to rule of law, each according to
   context and jurisdiction.

   Other Retribution:

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   System corruption: An attack where generated or modified traffic
   causes the corruption of a system.  This attack covers cases where a
   user's data may be lost or corrupted, and cases where a user's access
   to a system may be affected.

   Data loss, corruption:

   Denial of Service (by which self-censorship is covered):

   Emotional trauma:

2.  Safe Internet Measurement Guidelines

2.1.  Obtain consent

   Accountability and transparency are fundamentally related to consent.
   As per the Menlo Report, "Accountability demands that research
   methodology, ethical evaluations, data collected, and results
   generated should be documented and made available responsibly in
   accordance with balancing risks and benefits."[MenloReport] A user is
   best placed to balanced the risks and benefits for themselves
   therefore consent must be obtained.  From most transparent to least,
   there are a few options for obtaining consent.

2.1.1.  Informed Consent

   In an ideal world, informed consent would be collected from all users
   that may be placed at risk, no matter how small a risk, by an
   experiment.  In cases where it is practical to do so, this should be
   done.

   However for consent to be informed, all possible risks must be
   presented to the users.  The considerations in this document can be
   used to provide a starting point although other risks may be present
   depending on the nature of the measurements to be performed.

   Example: A researcher would like to use volunteer owned mobile
   devices to collect information about local Internet censorship.
   Connections will be attempted by the volunteer's device with services
   and content known or suspected to be subject to censorship orders.

   This experiment can carry substantial risk for the user depending on
   the circumstances, from disciplinary action from their employer to
   arrest or imprisonment.  Fully informed consent ensures that any risk
   that is being taken has been carefully considered by the volunteer
   before proceeding.

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2.1.2.  Proxy Consent

   In cases where it is not practical to collect informed consent from
   all users of a shared network, it may be possible to obtain proxy
   consent.  Proxy consent may be given by a network operator or
   employer that would be more familiar with the expectations of users
   of a network than the researcher.

   In some cases, a network operator or employer may have terms of
   service that specifically allow for giving consent to third parties
   to perform certain experiments.

   Example: A researcher would like to perform a packet capture to
   determine the TCP options and their values used by all client devices
   on an corporate wireless network.

   The employer may already have terms of service laid out that allow
   them to provide proxy consent for this experiment on behalf of the
   employees, in this case the users of the network.  The purpose of the
   experiment may affect whether or not they are able to provide this
   consent.  Say, performing engineering work on the network may be
   allowed, whereas academic research may not be already covered.

2.1.3.  Implied Consent

   In larger scale measurements, even proxy consent collection may not
   be practical.  In this case, implied consent may be presumed from
   users for some measurements.  Consider that users of a network will
   have certain expectations of privacy and those expectations may not
   align with the privacy guarantees offered by the technologies they
   are using.  As a thought experiment, consider how users might respond
   if asked for their informed consent for the measurements you'd like
   to perform.

   Implied consent should not be considered sufficient for any
   experiment that may collect sensitive or personally identifying
   information.  If practical, attempt to obtain informed consent or
   proxy consent from a sample of users to better understand the
   expectations of other users.

   Example: A researcher would like to run a measurement campaign to
   determine the maximum supported TLS version on popular web servers.

   The operator of a web server that is exposed to the Internet hosting
   a popular website would have the expectation that it may be included
   in surveys that look at supported protocols or extensions but would
   not expect that attempts be made to degrade the service with large
   numbers of simultaneous connections.

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   Example: A researcher would like to perform A/B testing for protocol
   feature and how it affects web performance.  They have created two
   versions of their software and have instrumented both to report
   telemetry back.  These updates will be pushed to users at random by
   the software's auto-update framework.  The telemetry consists only of
   performance metrics and does not contain any personally identifying
   or sensitive information.

   As users expect to receive automatic updates, the effect of changing
   the behaviour of the software is already expected by the user.  If
   users have already been informed that data will be reported back to
   the developers of the software, then again the addition of new
   metrics would be expected.  There are risks in pushing any new
   software update, and the A/B testing technique can reduce the number
   of users that may be adversely affected by a bad update.

   The reduced impact should not be used as an excuse for pushing higher
   risk updates, only updates that could be considered appropriate to
   push to all users should be A/B tested.  Likewise, not pushing the
   new behaviour to any user should be considered appropriate if some
   users are to remain with the old behavior.

   In the event that something does go wrong with the update, it should
   be easy for a user to discover that they have been part of an
   experiment and roll back the change, allowing for explicit refusal of
   consent to override the presumed implied consent.

2.2.  Isolate risk with a dedicated testbed

   Wherever possible, use a testbed.  An isolated network means that
   there are no other users sharing the infrastructure you are using for
   your experiments.

   When measuring performance, competing traffic can have negative
   effects on the performance of your test traffic and so the testbed
   approach can also produce more accurate and repeatable results than
   experiments using the public Internet.

   WAN link conditions can be emulated through artificial delays and/or
   packet loss using a tool like [netem].  Competing traffic can also be
   emulated using traffic generators.

2.3.  Be respectful of others' infrastructure

   If your experiment is designed to trigger a response from
   infrastructure that is not your own, consider what the negative
   consequences of that may be.  At the very least your experiment will
   consume bandwidth that may have to be paid for.

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   In more extreme circumstances, you could cause traffic to be
   generated that causes legal trouble for the owner of that
   infrastructure.  The Internet is a global network crossing many legal
   jurisdictions and so what may be legal for you is not necessarily
   legal for everyone.

   If you are sending a lot of traffic quickly, or otherwise generally
   deviate from typical client behaviour, a network may identify this as
   an attack which means that you will not be collecting results that
   are representative of what a typical client would see.

2.4.  Maintain a "Do Not Scan" list

   When performing active measurements on a shared network, maintain a
   list of hosts that you will never scan regardless of whether they
   appear in your target lists.  When developing tools for performing
   active measurement, or traffic generation for use in a larger
   measurement system, ensure that the tool will support the use of a
   "Do Not Scan" list.

   If complaints are made that request you do not generate traffic
   towards a host or network, you must add that host or network to your
   "Do Not Scan" list, even if no explanation is given or the request is
   automated.

   You may ask the requester for their reasoning if it would be useful
   to your experiment.  This can also be an opportunity to explain your
   research and offer to share any results that may be of interest.  If
   you plan to share the reasoning when publishing your measurement
   results, e.g. in an academic paper, you must seek consent for this
   from the requester.

   Be aware that in publishing your measurement results, it may be
   possible to infer your "Do Not Scan" list from those results.  For
   example, if you measured a well-known list of popular websites then
   it would be possible to correlate the results with that list to
   determine which are missing.

2.5.  Minimize data

   When collecting, using, disclosing, and storing data from a
   measurement, use only the minimal data necessary to perform a task.
   Reducing the amount of data reduces the amount of data that can be
   misused or leaked.

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   When deciding on the data to collect, assume that any data collected
   might be disclosed.  There are many ways that this could happen,
   through operation security mistakes or compulsion by a judicial
   system.

   When directly instrumenting a protocol to provide metrics to a
   passive observer, see section 6.1 of RFC6973[RFC6973] for the data
   minimalization considerations enumerated below that are specific to
   the use case.

2.5.1.  Discard it

   Discard data that is not required to perform the task.

   When performing active measurements be sure to only capture traffic
   that you have generated.  Traffic may be identified by IP ranges or
   by some token that is unlikely to be used by other users.

   Again, this can help to improve the accuracy and repeatability of
   your experiment.  [RFC2544], for performance benchmarking, requires
   that any frames received that were not part of the test traffic are
   discarded and not counted in the results.

2.5.2.  Mask it

   Mask data that is not required to perform the task.  Particularly
   useful for content of traffic to indicate that either a particular
   class of content existed or did not exist, or the length of the
   content, but not recording the content itself.  Can also replace
   content with tokens, or encrypt.

2.5.3.  Aggregate it

   When collecting data, consider if the granularity can be limited by
   using bins or adding noise.  XXX: Differential privacy.

   Do this at the source, definitely do it before you write to disk.

   [Tor.2017-04-001] presents a case-study on the in-memory statistics
   in the software used by the Tor network, as an example.

2.6.  Reduce accuracy

   Binning, categorizing, geoip, noise.

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2.7.  Analyze Risk

   The benefits should outweigh the risks.  Consider auxiliary data
   (e.g. third-party data sets) when assessing the risks.

3.  Security Considerations

   Take reasonable security precautions, e.g. about who has access to
   your data sets or experimental systems.

4.  IANA Considerations

   This document has no actions for IANA.

5.  Acknowledgements

   Many of these considerations are based on those from the
   [TorSafetyBoard] adapted and generalised to be applied to Internet
   research.

   Other considerations are taken from the Menlo Report [MenloReport]
   and its companion document [MenloReportCompanion].

6.  Informative References

   [netem]    Stephen, H., "Network emulation with NetEm", April 2005.

   [RFC2544]  Bradner, S. and J. McQuaid, "Benchmarking Methodology for
              Network Interconnect Devices", RFC 2544,
              DOI 10.17487/RFC2544, March 1999,
              <https://www.rfc-editor.org/info/rfc2544>.

   [TorSafetyBoard]
              Tor Project, "Tor Research Safety Board",
              <https://research.torproject.org/safetyboard/>.

   [RFC4949]  Shirey, R., "Internet Security Glossary, Version 2",
              August 2007, <https://www.rfc-editor.org/info/rfc4949>.

   [Tor.2017-04-001]
              Herm, K., "Privacy analysis of Tor's in-memory
              statistics", Tor Tech Report 2017-04-001, April 2017,
              <https://research.torproject.org/techreports/privacy-in-
              memory-2017-04-28.pdf>.

   [MenloReport]
              Dittrich, D. and E. Kenneally, "The Menlo Report: Ethical
              Principles Guiding Information and Communication

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              Technology Research", August 2012,
              <https://www.caida.org/publications/papers/2012/
              menlo_report_actual_formatted/>.

   [MenloReportCompanion]
              Bailey, M., Dittrich, D., and E. Kenneally, "Applying
              Ethical Principles to Information and Communication
              Technology Research", October 2013,
              <https://www.impactcybertrust.org/link_docs/Menlo-Report-
              Companion.pdf>.

   [RFC6973]  Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
              Morris, J., Hansen, M., and R. Smith, "Privacy
              Considerations for Internet Protocols", RFC 6973, July
              2013, <https://www.rfc-editor.org/info/rfc6937>.

   [SIGCOMM]  Jones, B., Ensafi, R., Feamster, N., Paxson, V., and N.
              Weaver, "Ethical Concerns for Censorship Measurement",
              August 2015,
              <http://conferences.sigcomm.org/sigcomm/2015/pdf/papers/
              nsethics/p17.pdf>.

Authors' Addresses

   Iain R. Learmonth
   HamBSD
   Email: irl@hambsd.org

   Gurshabad Grover
   Centre for Internet and Society
   Email: gurshabad@cis-india.org

   Mallory Knodel
   Center for Democracy and Technology
   Email: mknodel@cdt.org

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