Internet DRAFT - draft-irtf-pearg-safe-internet-measurement
draft-irtf-pearg-safe-internet-measurement
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
Task Force (IETF). Note that other groups may also distribute
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Internet-Drafts are draft documents valid for a maximum of six months
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material or to cite them other than as "work in progress."
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/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document.
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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