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2 Network Working Group I. Learmonth
3 Internet-Draft Tor Project
4 Intended status: Informational May 18, 2020
5 Expires: November 19, 2020
7 Guidelines for Performing Safe Measurement on the Internet
8 draft-irtf-pearg-safe-internet-measurement-03
10 Abstract
12 Researchers from industry and academia often use Internet
13 measurements as part of their work. While these measurements can
14 give insight into the functioning and usage of the Internet, they can
15 come at the cost of user privacy. This document describes guidelines
16 for ensuring that such measurements can be carried out safely.
18 Note
20 Comments are solicited and should be addressed to the research
21 group's mailing list at pearg@irtf.org and/or the author(s).
23 The sources for this draft are at:
25 https://github.com/irl/draft-safe-internet-measurement
27 Status of This Memo
29 This Internet-Draft is submitted in full conformance with the
30 provisions of BCP 78 and BCP 79.
32 Internet-Drafts are working documents of the Internet Engineering
33 Task Force (IETF). Note that other groups may also distribute
34 working documents as Internet-Drafts. The list of current Internet-
35 Drafts is at https://datatracker.ietf.org/drafts/current/.
37 Internet-Drafts are draft documents valid for a maximum of six months
38 and may be updated, replaced, or obsoleted by other documents at any
39 time. It is inappropriate to use Internet-Drafts as reference
40 material or to cite them other than as "work in progress."
42 This Internet-Draft will expire on November 19, 2020.
44 Copyright Notice
46 Copyright (c) 2020 IETF Trust and the persons identified as the
47 document authors. All rights reserved.
49 This document is subject to BCP 78 and the IETF Trust's Legal
50 Provisions Relating to IETF Documents
51 (https://trustee.ietf.org/license-info) in effect on the date of
52 publication of this document. Please review these documents
53 carefully, as they describe your rights and restrictions with respect
54 to this document.
56 1. Introduction
58 Performing research using the Internet, as opposed to an isolated
59 testbed or simulation platform, means that experiments co-exist in a
60 space with other users. This document outlines guidelines for
61 academic and industry researchers that might use the Internet as part
62 of scientific experimentation to mitigate risks to the safety of
63 other users.
65 1.1. Scope of this document
67 Following the guidelines contained within this document is not a
68 substitute for any institutional ethics review process, although
69 these guidelines could help to inform that process. Similarly, these
70 guidelines are not legal advice and local laws must also be
71 considered before starting any experiment that could have adverse
72 impacts on user safety.
74 The scope of this document is restricted to guidelines that mitigate
75 exposure to risks to Internet user safety when measuring properties
76 of the Internet: the network, its constiuent hosts and links, or its
77 users traffic.
79 For the purpose of this document, an Internet user is an individual
80 or organisation that uses the Internet to communicate, or maintains
81 Internet infrastructure.
83 1.2. Threat Model
85 A threat is a potential for a security violation, which exists when
86 there is a circumstance, capability, action, or event that could
87 breach security and cause harm [RFC4949]. Every Internet measurement
88 study has the potential to subject Internet users to threat actions,
89 or attacks.
91 Many of the threats to user safety occur from an instantiation (or
92 combination) of the following:
94 Surveillance: An attack whereby an Internet user's information is
95 collected. This type of attack covers not only data but also
96 metadata.
98 Inadequate protection of collected data: An attack where data, either
99 in flight or at rest, was not adequately protected from disclosure.
100 Failure to adequately protect data to the expectations of the user is
101 an attack even if it does not lead to another party gaining access to
102 the data.
104 Traffic generation: An attack whereby traffic is generated to
105 traverse the Internet.
107 Traffic modification: An attack whereby the Internet traffic of users
108 is modified.
110 Any conceivable Internet measurement study might be considered an
111 attack on an Internet user's safety. It is always necessary to
112 consider the best approach to mitigate the impact of measurements,
113 and to balance the risks of measurements against the benefits to
114 impacted users.
116 1.3. Measurement Studies
118 Internet measurement studies can be broadly categorized into two
119 groups: active measurements and passive measurements. Active
120 measurements generate or modify traffic while passive measurements
121 use surveillance of existing traffic. The type of measurement is not
122 truly binary and many studies will include both active and passive
123 components. The measurement of generated traffic may also lead to
124 insights into other users' traffic indirectly.
126 XXX On-path/off-path
128 XXX One ended/two ended
130 1.4. User Impact from Measurement Studies
132 Consequences of attacks
134 Breach of Privacy: data collection. This impact also covers the case
135 of an Internet user's data being shared beyond that which a user had
136 given consent for.
138 Impersonation: An attack where a user is impersonated during a
139 measurement.
141 XXX Legal
143 XXX Other Retribution
144 System corruption: An attack where generated or modified traffic
145 causes the corruption of a system. This attack covers cases where a
146 user's data may be lost or corrupted, and cases where a user's access
147 to a system may be affected.
149 XXX Data loss, corruption
151 XXX Denial of Service (by which self-censorship is covered)
153 XXX Emotional Trauma
155 2. Consent
157 XXX a user is best placed to balanced risks vs benefits themselves
159 In an ideal world, informed consent would be collected from all users
160 that may be placed at risk, no matter how small a risk, by an
161 experiment. In cases where it is practical to do so, this should be
162 done.
164 2.1. Informed Consent
166 For consent to be informed, all possible risks must be presented to
167 the users. The considerations in this document can be used to
168 provide a starting point although other risks may be present
169 depending on the nature of the measurements to be performed.
171 2.2. Informed Consent: Case Study
173 A researcher would like to use volunteer owned mobile devices to
174 collect information about local Internet censorship. Connections
175 will be made from the volunteer's device towards known or suspected
176 blocked webpages.
178 This experiment can carry substantial risk for the user depending on
179 the circumstances, from disciplinary action from their employer to
180 arrest or imprisonment. Fully informed consent ensures that any risk
181 that is being taken has been carefully considered by the volunteer
182 before proceeding.
184 2.3. Proxy Consent
186 In cases where it is not practical to collect informed consent from
187 all users of a shared network, it may be possible to obtain proxy
188 consent. Proxy consent may be given by a network operator or
189 employer that would be more familiar with the expectations of users
190 of a network than the researcher.
192 In some cases, a network operator or employer may have terms of
193 service that specifically allow for giving consent to 3rd parties to
194 perform certain experiments.
196 2.4. Proxy Consent: Case Study
198 A researcher would like to perform a packet capture to determine the
199 TCP options and their values used by all client devices on an
200 corporate wireless network.
202 The employer may already have terms of service laid out that allow
203 them to provide proxy consent for this experiment on behalf of the
204 employees (the users of the network). The purpose of the experiment
205 may affect whether or not they are able to provide this consent. For
206 example, to perform engineering work on the network then it may be
207 allowed, whereas academic research may not be covered.
209 2.5. Implied Consent
211 In larger scale measurements, even proxy consent collection may not
212 be practical. In this case, implied consent may be presumed from
213 users for some measurements. Consider that users of a network will
214 have certain expectations of privacy and those expectations may not
215 align with the privacy guarantees offered by the technologies they
216 are using. As a thought experiment, consider how users might respond
217 if asked for their informed consent for the measurements you'd like
218 to perform.
220 Implied consent should not be considered sufficient for any
221 experiment that may collect sensitive or personally identifying
222 information. If practical, attempt to obtain informed consent or
223 proxy consent from a sample of users to better understand the
224 expectations of other users.
226 2.6. Implied Consent: Case Study 1
228 A researcher would like to run a measurement campaign to determine
229 the maximum supported TLS version on popular web servers.
231 The operator of a web server that is exposed to the Internet hosting
232 a popular website would have the expectation that it may be included
233 in surveys that look at supported protocols or extensions but would
234 not expect that attempts be made to degrade the service with large
235 numbers of simultaneous connections.
237 2.7. Implied Consent: Case Study 2
239 A researcher would like to perform A/B testing for protocol feature
240 and how it affects web performance. They have created two versions
241 of their software and have instrumented both to report telemetry
242 back. These updates will be pushed to users at random by the
243 software's auto-update framework. The telemetry consists only of
244 performance metrics and does not contain any personally identifying
245 or sensitive information.
247 As users expect to receive automatic updates, the effect of changing
248 the behaviour of the software is already expected by the user. If
249 users have already been informed that data will be reported back to
250 the developers of the software, then again the addition of new
251 metrics would be expected. There are risks in pushing any new
252 software update, and the A/B testing technique can reduce the number
253 of users that may be adversely affected by a bad update.
255 The reduced impact should not be used as an excuse for pushing higher
256 risk updates, only updates that could be considered appropriate to
257 push to all users should be A/B tested. Likewise, not pushing the
258 new behaviour to any user should be considered appropriate if some
259 users are to remain with the old behavior.
261 In the event that something does go wrong with the update, it should
262 be easy for a user to discover that they have been part of an
263 experiment and roll back the change, allowing for explicit refusal of
264 consent to override the presumed implied consent.
266 3. Safety Considerations
268 3.1. Isolate risk with a dedicated testbed
270 Wherever possible, use a testbed. An isolated network means that
271 there are no other users sharing the infrastructure you are using for
272 your experiments.
274 When measuring performance, competing traffic can have negative
275 effects on the performance of your test traffic and so the testbed
276 approach can also produce more accurate and repeatable results than
277 experiments using the public Internet.
279 WAN link conditions can be emulated through artificial delays and/or
280 packet loss using a tool like [netem]. Competing traffic can also be
281 emulated using traffic generators.
283 3.2. Be respectful of other's infrastructure
285 If your experiment is designed to trigger a response from
286 infrastructure that is not your own, consider what the negative
287 consequences of that may be. At the very least your experiment will
288 consume bandwidth that may have to be paid for.
290 In more extreme circumstances, you could cause traffic to be
291 generated that causes legal trouble for the owner of that
292 infrastructure. The Internet is a global network crossing many legal
293 jurisdictions and so what may be legal for you is not necessarily
294 legal for everyone.
296 If you are sending a lot of traffic quickly, or otherwise generally
297 deviate from typical client behaviour, a network may identify this as
298 an attack which means that you will not be collecting results that
299 are representative of what a typical client would see.
301 3.2.1. Maintain a "Do Not Scan" list
303 When performing active measurements on a shared network, maintain a
304 list of hosts that you will never scan regardless of whether they
305 appear in your target lists. When developing tools for performing
306 active measurement, or traffic generation for use in a larger
307 measurement system, ensure that the tool will support the use of a
308 "Do Not Scan" list.
310 If complaints are made that request you do not generate traffic
311 towards a host or network, you must add that host or network to your
312 "Do Not Scan" list, even if no explanation is given or the request is
313 automated.
315 You may ask the requester for their reasoning if it would be useful
316 to your experiment. This can also be an opportunity to explain your
317 research and offer to share any results that may be of interest. If
318 you plan to share the reasoning when publishing your measurement
319 results, e.g. in an academic paper, you must seek consent for this
320 from the requester.
322 Be aware that in publishing your measurement results, it may be
323 possible to infer your "Do Not Scan" list from those results. For
324 example, if you measured a well-known list of popular websites then
325 it would be possible to correlate the results with that list to
326 determine which are missing.
328 3.3. Data Minimization
330 When collecting, using, disclosing, and storing data from a
331 measurement, use only the minimal data necessary to perform a task.
332 Reducing the amount of data reduces the amount of data that can be
333 misused or leaked.
335 When deciding on the data to collect, assume that any data collected
336 might be disclosed. There are many ways that this could happen,
337 through operation security mistakes or compulsion by a judicial
338 system.
340 When directly instrumenting a protocol to provide metrics to a
341 passive observer, see section 6.1 of RFC6973 [RFC6973] for data
342 minimalization considerations specific to this use case.
344 3.3.1. Discarding Data
346 XXX: Discard data that is not required to perform the task.
348 When performing active measurements be sure to only capture traffic
349 that you have generated. Traffic may be identified by IP ranges or
350 by some token that is unlikely to be used by other users.
352 Again, this can help to improve the accuracy and repeatability of
353 your experiment. [RFC2544], for performance benchmarking, requires
354 that any frames received that were not part of the test traffic are
355 discarded and not counted in the results.
357 3.3.2. Masking Data
359 XXX: Mask data that is not required to perform the task.
360 Particularly useful for content of traffic to indicate that either a
361 particular class of content existed or did not exist, or the length
362 of the content, but not recording the content itself. Can also
363 replace content with tokens, or encrypt.
365 3.3.3. Reduce Accuracy
367 XXX: Binning, categorizing, geoip, noise.
369 3.3.4. Data Aggregation
371 When collecting data, consider if the granularity can be limited by
372 using bins or adding noise. XXX: Differential privacy.
374 XXX: Do this at the source, definitely do it before you write to
375 disk.
377 [Tor.2017-04-001] presents a case-study on the in-memory statistics
378 in the software used by the Tor network, as an example.
380 4. Risk Analysis
382 The benefits should outweigh the risks. Consider auxiliary data
383 (e.g. third-party data sets) when assessing the risks.
385 5. Security Considerations
387 Take reasonable security precautions, e.g. about who has access to
388 your data sets or experimental systems.
390 6. IANA Considerations
392 This document has no actions for IANA.
394 7. Acknowledgements
396 Many of these considerations are based on those from the
397 [TorSafetyBoard] adapted and generalised to be applied to Internet
398 research.
400 Other considerations are taken from the Menlo Report [MenloReport]
401 and its companion document [MenloReportCompanion].
403 8. Informative References
405 [MenloReport]
406 Dittrich, D. and E. Kenneally, "The Menlo Report: Ethical
407 Principles Guiding Information and Communication
408 Technology Research", August 2012,
409 .
412 [MenloReportCompanion]
413 Bailey, M., Dittrich, D., and E. Kenneally, "Applying
414 Ethical Principles to Information and Communication
415 Technology Research", October 2013,
416 .
419 [netem] Stephen, H., "Network emulation with NetEm", April 2005.
421 [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for
422 Network Interconnect Devices", RFC 2544,
423 DOI 10.17487/RFC2544, March 1999,
424 .
426 [RFC4949] Shirey, R., "Internet Security Glossary, Version 2",
427 August 2007, .
429 [RFC6973] Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
430 Morris, J., Hansen, M., and R. Smith, "Privacy
431 Considerations for Internet Protocols", RFC 6973, July
432 2013, .
434 [Tor.2017-04-001]
435 Herm, K., "Privacy analysis of Tor's in-memory
436 statistics", Tor Tech Report 2017-04-001, April 2017,
437 .
440 [TorSafetyBoard]
441 Tor Project, "Tor Research Safety Board",
442 .
444 Author's Address
446 Iain R. Learmonth
447 Tor Project
449 Email: irl@torproject.org