Guidelines for Performing Safe Measurement on the Internet
draft-irtf-pearg-safe-internet-measurement-02

Network Working Group                                       I. Learmonth
Internet-Draft                                               Tor Project
Intended status: Informational                          December 4, 2019
Expires: June 6, 2020

       Guidelines for Performing Safe Measurement on the Internet
             draft-irtf-pearg-safe-internet-measurement-02

Abstract

   Researchers from industry and academia often use Internet
   measurements as part of their work.  While these measurements can
   give insight into the functioning and usage of the Internet, they can
   come at the cost of user privacy.  This document describes guidelines
   for ensuring that such measurements can be carried out safely.

Note

   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

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Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (https://trustee.ietf.org/license-info) in effect on the date of
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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 users.  This document outlines guidelines for
   academic and industry researchers that might use the Internet as part
   of scientific experimentation to mitigate risks to the safety of
   other users.

1.1.  Scope of this document

   Following the guidelines contained within this document is not a
   substitute for any institutional ethics review process, although
   these guidelines could help to inform that process.  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.

1.2.  Active and passive measurements

   Internet measurement studies can be broadly categorized into two
   groups: active measurements and passive measurements.  Active
   measurements generate traffic.  Performance measurements such as TCP
   throughput testing [RFC6349] or functional measurements such as the
   feature-dependent connectivity failure tests performed by
   [PATHspider] both fall into this category.  Performing passive
   measurements requires existing traffic.

   Both active and passive measurements carry risk.  A poorly considered
   active measurement could result in an inadvertent denial-of-service
   attack, while passive measurements could result in serious violations
   of user privacy.

   The type of measurement is not truly binary and many studies will
   include both active and passive components.  Each of the
   considerations in this document must be carefully considered for
   their applicability regardless of the type of measurement.

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2.  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.

2.1.  Informed Consent

   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.

2.2.  Informed Consent: Case Study

   A researcher would like to use volunteer owned mobile devices to
   collect information about local Internet censorship.  Connections
   will be made from the volunteer's device towards known or suspected
   blocked webpages.

   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.

2.3.  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 3rd parties to
   perform certain experiments.

2.4.  Proxy Consent: Case Study

   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 (the users of the network).  The purpose of the experiment

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   may affect whether or not they are able to provide this consent.  For
   example, to perform engineering work on the network then it may be
   allowed, whereas academic research may not be covered.

2.5.  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.

2.6.  Implied Consent: Case Study 1

   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.

2.7.  Implied Consent: Case Study 2

   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

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   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.

3.  Safety Considerations

3.1.  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.

3.2.  Be respectful of other's 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.

   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.

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3.2.1.  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.

3.3.  Data Minimization

   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.

   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 data
   minimalization considerations specific to this use case.

3.3.1.  Discarding Data

   XXX: Discard data that is not required to perform the task.

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   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.

3.3.2.  Masking Data

   XXX: 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.

3.3.3.  Reduce Accuracy

   XXX: Binning, categorizing, geoip, noise.

3.3.4.  Data Aggregation

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

   XXX: 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.

4.  Risk Analysis

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

5.  Security Considerations

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

6.  IANA Considerations

   This document has no actions for IANA.

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7.  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].

8.  Informative References

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

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

   [PATHspider]
              Learmonth, I., Trammell, B., Kuehlewind, M., and G.
              Fairhurst, "PATHspider: A tool for active measurement of
              path transparency", DOI 10.1145/2959424.2959441, July
              2016,
              <https://dl.acm.org/citation.cfm?doid=2959424.2959441>.

   [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>.

   [RFC6349]  Constantine, B., Forget, G., Geib, R., and R. Schrage,
              "Framework for TCP Throughput Testing", RFC 6349,
              DOI 10.17487/RFC6349, August 2011,
              <https://www.rfc-editor.org/info/rfc6349>.

   [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>.

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   [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>.

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

Author's Address

   Iain R. Learmonth
   Tor Project

   Email: irl@torproject.org

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