Considerations for Performing Safe Measurement on the Internet
draft-irtf-pearg-safe-internet-measurement-14

Network Working Group                                    I. R. Learmonth
Internet-Draft                                        SR2 Communications
Intended status: Informational                                 M. Knodel
Expires: 29 July 2026                                                   
                                                               G. Grover
                                                   internet Research Lab
                                                         25 January 2026

     Considerations for Performing Safe Measurement on the Internet
             draft-irtf-pearg-safe-internet-measurement-14

Abstract

   Internet measurement is important to researchers from industry,
   academia and civil society.  While measurement of the internet can
   give insight into the functioning and usage of the Internet, it can
   present risks to user privacy.  This document describes briefly those
   risks.  It also outlines considerations for researchers to reference
   when designing internet measurements to ensuring that those
   measurements can be carried out with user safety as a priority.

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/IRTF-PEARG/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.

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   This Internet-Draft will expire on 29 July 2026.

Copyright Notice

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   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Scope of this document  . . . . . . . . . . . . . . . . . . .   3
   3.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Potential user impact from measurement studies  . . . . . . .   4
   5.  Considerations  . . . . . . . . . . . . . . . . . . . . . . .   6
     5.1.  General Considerations  . . . . . . . . . . . . . . . . .   6
       5.1.1.  Analyze the risks and benefits  . . . . . . . . . . .   6
       5.1.2.  Transparency and accountability . . . . . . . . . . .   6
       5.1.3.  Identification of traffic . . . . . . . . . . . . . .   7
     5.2.  Obtaining consent from users  . . . . . . . . . . . . . .   7
       5.2.1.  Informed consent  . . . . . . . . . . . . . . . . . .   7
       5.2.2.  Proxy consent . . . . . . . . . . . . . . . . . . . .   8
       5.2.3.  Implied consent . . . . . . . . . . . . . . . . . . .   9
     5.3.  Data collection and sharing . . . . . . . . . . . . . . .  10
       5.3.1.  Data collection and minimiszation . . . . . . . . . .  10
       5.3.2.  Data Sharing  . . . . . . . . . . . . . . . . . . . .  12
     5.4.  Impact on the network . . . . . . . . . . . . . . . . . .  12
       5.4.1.  Impact on others' infrastructure  . . . . . . . . . .  13
       5.4.2.  Maintain a "Do Not Scan" list . . . . . . . . . . . .  13
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  14
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  14
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  14
   9.  Informative References  . . . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

1.  Introduction

   Measurement of the internet provides important insights and is a
   growing area of research.  Similarly the internet plays a role in
   enhancing research methods of different kinds.

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   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.  Researchers using the
   Internet as part of a scientific experiment include academic,
   industry and civil society researchers.  This document outlines
   considerations for such researchers so they may mitigate risks to the
   safety of end users and other services.

   It is common today for certain internet measurements to require an
   ethics review before gaining approval and to report details of that
   review in the resulting work.  Previous work in the area of ethical
   concerns includes the Menlo Report [MenloReport] and its companion
   document [MenloReportCompanion], [Ethical Concerns for Censorship
   Measurement] as presented at SIGGCOMM, and [Operationalizing
   Cybersecurity Research Ethics Review: From Principles and Guidelines
   to Practice] as an example of ethical guidelines.  Many organizations
   publish specific guidelines around safe measurements e.g.
   [TorSafetyBoard].

   The measurement landscape and community is wide and varied.  The
   specific context for Internet measurements should be taken into
   account by researchers/reviewers and applied appropriately.  This
   document does not attempt to supersede any recommendations such as
   those cited above, but rather to act as a point of reference for high
   level considerations with particular emphasis on user consent and
   data collection.  In the growing area of research that includes
   internet measurement we see this work as part of a larger effort to
   better equip review boards to evaluate internet measurement methods.
   Future work may specialize these considerations for specific types or
   contexts of internet measurement.

2.  Scope of this document

   The document contains considerations for how to measure the internet
   while prioritizing user safety.  The considerations are particularly
   germane to projects that involve the generation, collection and/or
   analysis of traffic from humans.  When performing research on a
   platform shared with live traffic from users, that research is
   considered safe if and only if the users and others are protected
   from or unlikely to experience danger, risk, or injury arising due to
   the research, now or in the future.

   *  The considerations presented here are not an exhaustive list.
      Depending on the measurement there may be other factors that need
      to be taken into account when evaluating the safety of the
      measurement.

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   *  Following the considerations contained within this document are
      not a substitute for institutional ethics review processes,
      although these considerations could help to inform that process.

   *  Similarly, these considerations are not legal advice.  Local laws
      must be considered before starting any experiment that could have
      adverse impacts on user safety.

   *  The scope of this document is restricted to considerations that
      mitigate exposure to risks to user safety when measuring
      properties of the internet: the network, its constituent hosts and
      links, or user traffic.

3.  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].

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

   Active measurement: Active measurements generate or modify traffic.

   Passive measurement: Passive measurements involve the observation of
   existing traffic without active intervention.

   On/off-path: A measurement that is on-path happens on the network.
   Off-path indicates activity in a side-channel, end-point or at other
   points where the user, their connection, or their data can be
   accessed.

   One-/two-ended: A one-ended measurement is like a probe or a trace.
   A measurement with two-ended control requires the cooperation of both
   endpoints, which might include the network itself if that is the
   measurement target.

4.  Potential user impact from measurement studies

   Any conceivable internet measurement study might have an impact 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.

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   Some possible ways in which users can be affected as a result of an
   internet measurement study:

   Breach of privacy: User privacy can be affected or violated in many
   ways.  The first consideration is at the stage of data collection
   itself.  First-order data, such as name, can distinguish a person.
   Second-order data, such as IP address, can be used to track
   behaviour.  The second privacy risk is an internet user's data being
   shared beyond that for which a user had given consent, through
   accidental, malicious or compelled disclosure.  A third privacy
   consideration relates to the risk of re-identification or de-
   anonymization of obfuscated or masked user data.  [Kenneally]

   Inadequate data protection: A scenario where data, either in transit
   or at rest, lacks sufficient protection from disclosure.  Failure to
   meet user expectations for data protection is a concern, even if it
   does not result in unauthorized access to the data.  This includes
   cases of improper access control (i.e. people having access to user
   data who do not need it).

   Traffic generation: A scenario where undue traffic is generated to
   traverse the internet.

   Traffic modification: A scenario where users' on-path internet
   traffic is non-consensually modified.

   Impersonation: A scenario 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 laws, each according to context and
   jurisdiction.  A measurement study may violate these policies, and
   the consequences of such a violation may be severe.  At the same
   time, legal requests may compel disclosure of sensitive data to
   government entities or to courts in lawsuits.  [Kenneally]

   Unavailability: Users or other entities may rely on the information
   or systems that are involved in the research and they may be harmed
   by unexpected or planned unavailability of that information or
   systems [Menlo].

   System or data corruption: A scenario where generated or modified
   traffic causes the corruption of a system.  This 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 as a result.

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   Emotional trauma: A scenario where a measurement of or exposure to
   content or behaviour in an internet measurement study causes a user
   emotional or psychological harm.

5.  Considerations

5.1.  General Considerations

5.1.1.  Analyze the risks and benefits

   The benefits of internet measurement should outweigh the risks.
   [Menlo] [Kenneally]

   Internet measurements studies should generally advance knowledge and
   create meaningful results.  Benefits of internet measurements studies
   can include but are not limited to analysis of internet topology;
   understanding the effect or prevalance of new internet technologies,
   protocols or applications; characterisation or prevention of malware
   or other security and privacy threats, and much more.  [Kenneally]

   The risks of internet measurement studies, particularly to user
   safety, are summarised in Section 1.3.  Consider those risks, but
   also of auxiliary data (e.g. third-party data sets).  Note that while
   a privacy risk may not be immediately apparent or realisable, in the
   future increased computing power may then make something possible.

   Ensuring that a project's benefits outweigh its risks means the
   application of the considerations, but also through regular community
   feedback (particularly from those who will ostensibly benefit or be
   at risk through the project), proper auditing of the study, and
   impact assessments.

   Example: A research project releases encrypted payloads as a method
   for minimising exposure of sensitive user data.  However the
   encryption could be trivially broken in the future with typical
   increases in computing power.

5.1.2.  Transparency and accountability

   Have “a general policy of openness about developments, practices and
   policies with respect to personal data.” [OECD]

   Despite best intentions, things fall apart.  In the case of any
   adverse events, researchers should “responsibly inform affected
   stakeholders.” [Menlo] Where contacting users is not feasible,
   information about discovery of security vulnerabilities, improvements
   and fixes, data compromise, instances of unauthorized access to
   information should be published publicly.

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   If possible, institute a regular accountability and reporting
   mechanism, such as an annual review by an advisory board.  Such
   regular accountability mechanisms can include an assessment of the
   experiment's impact on user safety and privacy, analysis of security
   events, and an evaluation of whether the experiment is meeting its
   expected goals.

5.1.3.  Identification of traffic

   Proactively identify your measurement to others on the network.
   "This allows any party or organization to understand what an
   unsolicited probe packet is, what its purpose is, and, most
   importantly, who to contact."[RFC9511]

   Example: For a layer 3 IP packet probe you could mark measurements
   with a probe description URI as defined in RFC9511.

   This guideline may be ignored if and only if attribution runs
   contrary to the purpose of the measurement study or would otherwise
   render it ineffectual.  For example, for an experiment collecting
   information about local internet censorship, the network may provide
   different results if the connections can be identified as serving
   that purpose.

5.2.  Obtaining consent from users

   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."  [Menlo] A user is
   best placed to balance the risks and benefits for themselves
   therefore consent must be obtained.  From most transparent to least,
   there are a few options for obtaining consent.

5.2.1.  Informed consent

   Informed consent should be collected from all users that may be
   placed at risk by an experiment.

   For consent to be informed, a reasonable coverage of 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.
   In addition, it should be clear from the language of consent request
   who the asker is, and what the terms of data observation and/or
   collection are.

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   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
   their specific circumstances.  Trying to access censored material can
   be seen as (network) policy infringement or breaking laws.
   Consequences can range from disciplinary action from their employer
   to imprisonment by government authorities.  If the experimenter wants
   to expose volunteers to this kind of risk, users must be fully
   informed, and voluntarily give consent to run the measurement.  Even
   then, experimenters should seriously consider designing their
   experiment in another way.

   Note that informed consent is notoriously tricky to obtain.
   Conveying all possible risks of a measurement is often simply
   impractical, depending upon how technical the user audience is, the
   context of the consent prompt, what the tool is normally used by
   users for, etc.  In addition, consent can have network effects.  For
   example, asking a user to consent to sharing information about their
   communication with others can have impacts on users who have not
   personally consented to the study.

5.2.2.  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: Some researchers would like to perform a packet capture to
   determine the TCP options and their values used by all client devices
   on a 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.

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   Example: A research project looks at networked "things", yet users'
   only interface with the network is through a device that does not
   provide interaction to the degree that would be sufficient to obtain
   informed consent at time of use.

   However in this case the user can be informed of the use of data for
   internet measurement research in the device's terms of use and
   privacy notice, which can be included in a printed, physical manual
   for the device or accessed at any time via a webpage.  These are
   examples of proxy consent such that the device manufacturer may
   choose to share data under certain specified conditions, or to
   conduct their own measurements.

5.2.3.  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.

   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.

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   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.  Note that the reduced impact of A/B
   testing should not be used be an excuse to push updates that might
   compromise user expectations around security and privacy.

   In the event that something does go wrong with the update, it should
   be easy for users 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.

5.3.  Data collection and sharing

5.3.1.  Data collection and minimiszation

   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 operational security mistakes or compulsion by a state
   authority.

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

5.3.1.1.  Collect the minimum amount of data

   Collect only that data that is required to perform the study and
   discard data that is not required.

   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.  For performance benchmarking, [RFC2544] requires
   that any frames received that were not part of the test traffic are
   discarded and not counted in the results.

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5.3.1.2.  Store data securely

   Data should be stored in a secure location, with appropriate access
   controls and full-disk encryption if possible.  Access to the
   measurement data should be minimized to only those personnel who
   require access to perform research.

5.3.1.3.  Minimization techniques

   For any data collected evaluate which minimization techniques can and
   should be applied to that data in order to minimize the risk to users
   whilst still safely providing sufficient data for the measurement to
   be effective.  A range of pseudo-anonymization or anonymization
   techniques are available [ADD REFERENCES].  IP addresses are
   particularly identifying...

5.3.1.3.1.  Mask data

   Mask data that is not required to perform the task.  This technique
   is 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.  The content
   can be replaced with tokens or encrypted.

   It is important to note that masking data does not necessarily
   anonymize it [SurveyNetworkTrafficAnonymisationTech].

5.3.1.3.2.  Aggregate data

   When collecting data, consider if the granularity can be limited by
   using bins or adding noise.  Differential privacy techniques
   [DifferentialPrivacy] can help with this.

   Example: [Tor.2017-04-001] presents a case-study on the in-memory
   statistics in the software used by the Tor network.

5.3.1.3.3.  Reduce the accuracy

   There are various techniques that can be used to reduce the accuracy
   of the collected data and make it less identifying while still
   meeting the needs of the measurement.

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   The use of binning to group numbers of more-or-less continuous
   values, coarse categorization in modeling, reduction in
   concentrations of IP address by geography (geoip) or other first- or
   second-order identifiers, the introduction of noise and all privacy-
   preserving measurement techniques that allow researchers to safely
   conduct internet measurement experiments without risking harm to real
   users [Janson].

5.3.2.  Data Sharing

   Further to use of measurement data, data is often shared with other
   researchers.  Measurement data sharing comes with its own set of
   expectations and responsibilities of the provider.  Likewise there
   are responsibilities that come with the use of others’ measurement
   data.  One obvious expectation is around end-user consent (see
   "Implied consent" above).  Allman and Paxson [Allman] provide "a set
   of guidelines that aim to aid the process of sharing measurement
   data... [in] a framework under which providers and users can better
   attain a mutual understanding about how to treat particular
   datasets."

   Their guidance for data providers is to:

   *  explicitly indicate the terms of a dataset’s acceptable use

   *  convey what interactions they desire or will accommodate.

   Their guidance for researchers is to:

   *  be thoughtful in the reporting of potentially sensitive
      information gleaned from providers’ data.

   *  comply with the indications and interactions of the data
      providers.

   Example: Researchers have obtained network measurement data from more
   than one provider for purposes of conducting analysis of protocol use
   on both.  Where privacy paritioning techniques are used, the
   researchers' findings may inadvertently collude to uncover private
   information about users.  Once realised, researchers should mitigate
   this privacy risk to end users as well as disclosing this result to
   the data providers themselves.

5.4.  Impact on the network

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5.4.1.  Impact on 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.

   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 that crosses many
   legal jurisdictions and so what may be legal for one is not
   necessarily legal for another.

   If you are sending a lot of traffic quickly, or otherwise generally
   deviating 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.

   One possible way to mitigate this risk is transparency, i.e. mark
   measurement-related data or activity as such.  For example, the
   popular internet measurement tool ZMap hardcodes its packets to have
   IP ID 54321 in order to allow identification [ZMap].

5.4.2.  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 should add that host or network to
   your "Do Not Scan" list, even if the request is automated.  However
   it seems reasonable that these requests could be evaluated in
   context.  (For example in censorship measurement it would be
   commensurate to ensure the request isn't an attempt to hide the fact
   that a certain target is unreachable in a particular geography.)

   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.

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   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.  This inference might leak the fact that
   those websites specifically requested to not be scanned.

   On the other hand there may be benefits to publish your "Do Not Scan"
   list including the opportunity to reconsider the list over time,
   invite feedback, ensure research reproducibility, or bootstrapping
   other measurement projects or tools.

6.  Security Considerations

   This document as a whole addresses user safety considerations for
   internet measurement studies, and thus discusses security
   considerations extensively throughout regarding collection and
   storage of user data.

7.  IANA Considerations

   This document has no actions for IANA.

8.  Acknowledgements

   Many of these considerations are based on those from the
   [TorSafetyBoard] adapted and generalized to be applied to internet
   research.  Other considerations are taken from the Menlo Report
   [Menlo] and its companion document [MenloReportCompanion], and
   research and discussions of the internet measurement and ethics
   communities.

   Comments and contributions from Marwan Fayed, Jeroen van der Ham,
   Arturo Filastò, Christian Huitema, Tobias Fiebig, Greg Skinner,
   Oliver Gasser, Craig Partridge, Eric Rescorla and Shivan Kaul Sahib
   greatly improved this document.

9.  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>.

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   [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>.

   [Menlo]    Dittrich, D. and E. Kenneally, "The Menlo Report: Ethical
              Principles Guiding Information and Communication
              Technology Research", August 2012,
              <https://www.dhs.gov/sites/default/files/publications/CSD-
              MenloPrinciplesCORE-20120803_1.pdf>.

   [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>.

   [DifferentialPrivacy]
              Dwork, C., McSherry, F., Nissim, K., and A. Smith,
              "Calibrating Noise to Sensitivity in Private Data
              Analysis", 2006,
              <https://link.springer.com/chapter/10.1007/11681878_14>.

   [SurveyNetworkTrafficAnonymisationTech]
              Van Dijkhuizen, N. and J. Van Der Ham, "A Survey of
              Network Traffic Anonymisation Techniques and
              Implementations", May 2018,
              <https://dl.acm.org/doi/10.1145/3182660>.

   [OECD]     OECD, "OECD Privacy Principles", 1980,
              <http://oecdprivacy.org/>.

   [ZMap]     University of Michigan, "ZMap Source Code - packet.c",
              <https://github.com/zmap/zmap/blob/main/src/probe_modules/
              packet.c>.

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   [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>.

   [RFC9511]  Vyncke, É., Donnet, B., and J. Iurman, "Attribution of
              Internet Probes", November 2023,
              <https://www.rfc-editor.org/info/rfc9511>.

   [Allman]   Allman, M. and V. Paxson, "Issues and Etiquette Concerning
              Use of Shared Measurement Data", October 2007,
              <https://conferences.sigcomm.org/imc/2007/papers/
              imc80.pdf>.

   [caida]    CAIDA, "Promotion of Data Sharing", January 2010,
              <https://www.caida.org/catalog/datasets/sharing>.

   [Kenneally]
              Kenneally, E. and K. Claffy, "Dialing privacy and utility:
              a proposed data-sharing framework to advance Internet
              research", 2010, <https://www.caida.org/catalog/
              papers/2010_dialing_privacy_utility/
              dialing_privacy_utility.pdf>.

   [Janson]   Janson, R., Traudt, M., and N. Hopper, "Privacy-Preserving
              Dynamic Learning of Tor Network Traffic", 2010,
              <https://dl.acm.org/doi/pdf/10.1145/3243734.3243815>.

Authors' Addresses

   Iain R. Learmonth
   SR2 Communications
   Email: irl@sr2.uk

   Mallory Knodel
   Email: mallory.knodel@nyu.edu

   Gurshabad Grover
   internet Research Lab
   Email: gurshabad@irl.works

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