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Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 Network Working Group I. Learmonth 3 Internet-Draft Tor Project 4 Intended status: Informational July 7, 2019 5 Expires: January 8, 2020 7 Guidelines for Performing Safe Measurement on the Internet 8 draft-irtf-pearg-safe-internet-measurement-00 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 January 8, 2020. 44 Copyright Notice 46 Copyright (c) 2019 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 1.2. Active and passive measurements 76 Internet measurement studies can be broadly categorized into two 77 groups: active measurements and passive measurements. Active 78 measurements generate traffic. Performance measurements such as TCP 79 throughput testing [RFC6349] or functional measurements such as the 80 feature-dependent connectivity failure tests performed by 81 [PATHspider] both fall into this category. Performing passive 82 measurements requires existing traffic. 84 Both active and passive measurements carry risk. A poorly considered 85 active measurement could result in an inadvertent denial-of-service 86 attack, while passive measurements could result in serious violations 87 of user privacy. 89 The type of measurement is not truly binary and many studies will 90 include both active and passive components. Each of the 91 considerations in this document must be carefully considered for 92 their applicability regardless of the type of measurement. 94 2. Consent 96 In an ideal world, informed consent would be collected from all users 97 that may be placed at risk, no matter how small a risk, by an 98 experiment. In cases where it is practical to do so, this should be 99 done. 101 2.1. Informed Consent 103 For consent to be informed, all possible risks must be presented to 104 the users. The considerations in this document can be used to 105 provide a starting point although other risks may be present 106 depending on the nature of the measurements to be performed. 108 2.2. Informed Consent: Case Study 110 A researcher would like to use volunteer owned mobile devices to 111 collect information about local Internet censorship. Connections 112 will be made from the volunteer's device towards known or suspected 113 blocked webpages. 115 This experiment can carry substantial risk for the user depending on 116 the circumstances, from disciplinary action from their employer to 117 arrest or imprisonment. Fully informed consent ensures that any risk 118 that is being taken has been carefully considered by the volunteer 119 before proceeding. 121 2.3. Proxy Consent 123 In cases where it is not practical to collect informed consent from 124 all users of a shared network, it may be possible to obtain proxy 125 consent. Proxy consent may be given by a network operator or 126 employer that would be more familiar with the expectations of users 127 of a network than the researcher. 129 In some cases, a network operator or employer may have terms of 130 service that specifically allow for giving consent to 3rd parties to 131 perform certain experiments. 133 2.4. Proxy Consent: Case Study 135 A researcher would like to perform a packet capture to determine the 136 TCP options and their values used by all client devices on an 137 corporate wireless network. 139 The employer may already have terms of service laid out that allow 140 them to provide proxy consent for this experiment on behalf of the 141 employees (the users of the network). The purpose of the experiment 142 may affect whether or not they are able to provide this consent. For 143 example, to perform engineering work on the network then it may be 144 allowed, whereas academic research may not be covered. 146 2.5. Implied Consent 148 In larger scale measurements, even proxy consent collection may not 149 be practical. In this case, implied consent may be presumed from 150 users for some measurements. Consider that users of a network will 151 have certain expectations of privacy and those expectations may not 152 align with the privacy guarantees offered by the technologies they 153 are using. As a thought experiment, consider how users might respond 154 if asked for their informed consent for the measurements you'd like 155 to perform. 157 Implied consent should not be considered sufficient for any 158 experiment that may collect sensitive or personally identifying 159 information. If practical, attempt to obtain informed consent or 160 proxy consent from a sample of users to better understand the 161 expectations of other users. 163 2.6. Implied Consent: Case Study 1 165 A researcher would like to run a measurement campaign to determine 166 the maximum supported TLS version on popular web servers. 168 The operator of a web server that is exposed to the Internet hosting 169 a popular website would have the expectation that it may be included 170 in surveys that look at supported protocols or extensions but would 171 not expect that attempts be made to degrade the service with large 172 numbers of simultaneous connections. 174 2.7. Implied Consent: Case Study 2 176 A researcher would like to perform A/B testing for protocol feature 177 and how it affects web performance. They have created two versions 178 of their software and have instrumented both to report telemetry 179 back. These updates will be pushed to users at random by the 180 software's auto-update framework. The telemetry consists only of 181 performance metrics and does not contain any personally identifying 182 or sensitive information. 184 As users expect to receive automatic updates, the effect of changing 185 the behaviour of the software is already expected by the user. If 186 users have already been informed that data will be reported back to 187 the developers of the software, then again the addition of new 188 metrics would be expected. There are risks in pushing any new 189 software update, and the A/B testing technique can reduce the number 190 of users that may be adversely affected by a bad update. 192 The reduced impact should not be used as an excuse for pushing higher 193 risk updates, only updates that could be considered appropriate to 194 push to all users should be A/B tested. Likewise, not pushing the 195 new behaviour to any user should be considered appropriate if some 196 users are to remain with the old behavior. 198 In the event that something does go wrong with the update, it should 199 be easy for a user to discover that they have been part of an 200 experiment and roll back the change, allowing for explicit refusal of 201 consent to override the presumed implied consent. 203 3. Safety Considerations 205 3.1. Use a testbed 207 Wherever possible, use a testbed. An isolated network means that 208 there are no other users sharing the infrastructure you are using for 209 your experiments. 211 When measuring performance, competing traffic can have negative 212 effects on the performance of your test traffic and so the testbed 213 approach can also produce more accurate and repeatable results than 214 experiments using the public Internet. 216 WAN link conditions can be emulated through artificial delays and/or 217 packet loss using a tool like [netem]. Competing traffic can also be 218 emulated using traffic generators. 220 3.2. Only record your own traffic 222 When performing active measurements be sure to only capture traffic 223 that you have generated. Traffic may be identified by IP ranges or 224 by some token that is unlikely to be used by other users. 226 Again, this can help to improve the accuracy and repeatability of 227 your experiment. [RFC2544], for performance benchmarking, requires 228 that any frames received that were not part of the test traffic are 229 discarded and not counted in the results. 231 3.3. Be respectful of other's infrastructure 233 If your experiment is designed to trigger a response from 234 infrastructure that is not your own, consider what the negative 235 consequences of that may be. At the very least your experiment will 236 consume bandwidth that may have to be paid for. 238 In more extreme circumstances, you could cause traffic to be 239 generated that causes legal trouble for the owner of that 240 infrastructure. The Internet is a global network crossing many legal 241 jurisdictions and so what may be legal for you is not necessarily 242 legal for everyone. 244 If you are sending a lot of traffic quickly, or otherwise generally 245 deviate from typical client behaviour, a network may identify this as 246 an attack which means that you will not be collecting results that 247 are representative of what a typical client would see. 249 3.3.1. Maintain a "Do Not Scan" list 251 When performing active measurements on a shared network, maintain a 252 list of hosts that you will never scan regardless of whether they 253 appear in your target lists. When developing tools for performing 254 active measurement, or traffic generation for use in a larger 255 measurement system, ensure that the tool will support the use of a 256 "Do Not Scan" list. 258 If complaints are made that request you do not generate traffic 259 towards a host or network, you must add that host or network to your 260 "Do Not Scan" list, even if no explanation is given or the request is 261 automated. 263 You may ask the requester for their reasoning if it would be useful 264 to your experiment. This can also be an opportunity to explain your 265 research and offer to share any results that may be of interest. If 266 you plan to share the reasoning when publishing your measurement 267 results, e.g. in an academic paper, you must seek consent for this 268 from the requester. 270 Be aware that in publishing your measurement results, it may be 271 possible to infer your "Do Not Scan" list from those results. For 272 example, if you measured a well-known list of popular websites then 273 it would be possible to correlate the results with that list to 274 determine which are missing. 276 3.4. Only collect data that is safe to make public 278 When deciding on the data to collect, assume that any data collected 279 might become public. There are many ways that this could happen, 280 through operation security mistakes or compulsion by a judicial 281 system. 283 3.5. Minimization 285 For all data collected, consider whether or not it is really needed. 287 3.6. Aggregation 289 When collecting data, consider if the granularity can be limited by 290 using bins or adding noise. XXX: Differential privacy. 292 3.7. Source Aggregation 294 Do this at the source, definitely do it before you write to disk. 296 [Tor.2017-04-001] presents a case-study on the in-memory statistics 297 in the software used by the Tor network, as an example. 299 4. Risk Analysis 301 The benefits should outweigh the risks. Consider auxiliary data 302 (e.g. third-party data sets) when assessing the risks. 304 5. Security Considerations 306 Take reasonable security precautions, e.g. about who has access to 307 your data sets or experimental systems. 309 6. IANA Considerations 311 This document has no actions for IANA. 313 7. Acknowledgements 315 Many of these considerations are based on those from the 316 [TorSafetyBoard] adapted and generalised to be applied to Internet 317 research. 319 Other considerations are taken from the Menlo Report [MenloReport] 320 and its companion document [MenloReportCompanion]. 322 8. Informative References 324 [MenloReport] 325 Dittrich, D. and E. Kenneally, "The Menlo Report: Ethical 326 Principles Guiding Information and Communication 327 Technology Research", August 2012, 328 . 331 [MenloReportCompanion] 332 Bailey, M., Dittrich, D., and E. Kenneally, "Applying 333 Ethical Principles to Information and Communication 334 Technology Research", October 2013, 335 . 338 [netem] Stephen, H., "Network emulation with NetEm", April 2005. 340 [PATHspider] 341 Learmonth, I., Trammell, B., Kuehlewind, M., and G. 342 Fairhurst, "PATHspider: A tool for active measurement of 343 path transparency", DOI 10.1145/2959424.2959441, July 344 2016, 345 . 347 [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for 348 Network Interconnect Devices", RFC 2544, 349 DOI 10.17487/RFC2544, March 1999, 350 . 352 [RFC6349] Constantine, B., Forget, G., Geib, R., and R. Schrage, 353 "Framework for TCP Throughput Testing", RFC 6349, 354 DOI 10.17487/RFC6349, August 2011, 355 . 357 [Tor.2017-04-001] 358 Herm, K., "Privacy analysis of Tor's in-memory 359 statistics", Tor Tech Report 2017-04-001, April 2017, 360 . 363 [TorSafetyBoard] 364 Tor Project, "Tor Research Safety Board", 365 . 367 Author's Address 369 Iain R. Learmonth 370 Tor Project 372 Email: irl@torproject.org