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Checking references for intended status: Informational ---------------------------------------------------------------------------- No issues found here. Summary: 0 errors (**), 0 flaws (~~), 1 warning (==), 1 comment (--). 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 8, 2019 5 Expires: January 9, 2020 7 Guidelines for Performing Safe Measurement on the Internet 8 draft-irtf-pearg-safe-internet-measurement-01 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 9, 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. Isolate risk with a dedicated 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. Be respectful of other's infrastructure 222 If your experiment is designed to trigger a response from 223 infrastructure that is not your own, consider what the negative 224 consequences of that may be. At the very least your experiment will 225 consume bandwidth that may have to be paid for. 227 In more extreme circumstances, you could cause traffic to be 228 generated that causes legal trouble for the owner of that 229 infrastructure. The Internet is a global network crossing many legal 230 jurisdictions and so what may be legal for you is not necessarily 231 legal for everyone. 233 If you are sending a lot of traffic quickly, or otherwise generally 234 deviate from typical client behaviour, a network may identify this as 235 an attack which means that you will not be collecting results that 236 are representative of what a typical client would see. 238 3.2.1. Maintain a "Do Not Scan" list 240 When performing active measurements on a shared network, maintain a 241 list of hosts that you will never scan regardless of whether they 242 appear in your target lists. When developing tools for performing 243 active measurement, or traffic generation for use in a larger 244 measurement system, ensure that the tool will support the use of a 245 "Do Not Scan" list. 247 If complaints are made that request you do not generate traffic 248 towards a host or network, you must add that host or network to your 249 "Do Not Scan" list, even if no explanation is given or the request is 250 automated. 252 You may ask the requester for their reasoning if it would be useful 253 to your experiment. This can also be an opportunity to explain your 254 research and offer to share any results that may be of interest. If 255 you plan to share the reasoning when publishing your measurement 256 results, e.g. in an academic paper, you must seek consent for this 257 from the requester. 259 Be aware that in publishing your measurement results, it may be 260 possible to infer your "Do Not Scan" list from those results. For 261 example, if you measured a well-known list of popular websites then 262 it would be possible to correlate the results with that list to 263 determine which are missing. 265 3.3. Data Minimization 267 When collecting, using, disclosing, and storing data from a 268 measurement, use only the minimal data necessary to perform a task. 269 Reducing the amount of data reduces the amount of data that can be 270 misused or leaked. 272 When deciding on the data to collect, assume that any data collected 273 might be disclosed. There are many ways that this could happen, 274 through operation security mistakes or compulsion by a judicial 275 system. 277 When directly instrumenting a protocol to provide metrics to a 278 passive observer, see section 6.1 of RFC6973 [RFC6973] for data 279 minimalization considerations specific to this use case. 281 3.3.1. Discarding Data 283 XXX: Discard data that is not required to perform the task. 285 When performing active measurements be sure to only capture traffic 286 that you have generated. Traffic may be identified by IP ranges or 287 by some token that is unlikely to be used by other users. 289 Again, this can help to improve the accuracy and repeatability of 290 your experiment. [RFC2544], for performance benchmarking, requires 291 that any frames received that were not part of the test traffic are 292 discarded and not counted in the results. 294 3.3.2. Masking Data 296 XXX: Mask data that is not required to perform the task. 297 Particularly useful for content of traffic to indicate that either a 298 particular class of content existed or did not exist, or the length 299 of the content, but not recording the content itself. Can also 300 replace content with tokens, or encrypt. 302 3.3.3. Reduce Accuracy 304 XXX: Binning, categorizing, geoip, noise. 306 3.3.4. Data Aggregation 308 When collecting data, consider if the granularity can be limited by 309 using bins or adding noise. XXX: Differential privacy. 311 XXX: Do this at the source, definitely do it before you write to 312 disk. 314 [Tor.2017-04-001] presents a case-study on the in-memory statistics 315 in the software used by the Tor network, as an example. 317 4. Risk Analysis 319 The benefits should outweigh the risks. Consider auxiliary data 320 (e.g. third-party data sets) when assessing the risks. 322 5. Security Considerations 324 Take reasonable security precautions, e.g. about who has access to 325 your data sets or experimental systems. 327 6. IANA Considerations 329 This document has no actions for IANA. 331 7. Acknowledgements 333 Many of these considerations are based on those from the 334 [TorSafetyBoard] adapted and generalised to be applied to Internet 335 research. 337 Other considerations are taken from the Menlo Report [MenloReport] 338 and its companion document [MenloReportCompanion]. 340 8. Informative References 342 [MenloReport] 343 Dittrich, D. and E. Kenneally, "The Menlo Report: Ethical 344 Principles Guiding Information and Communication 345 Technology Research", August 2012, 346 . 349 [MenloReportCompanion] 350 Bailey, M., Dittrich, D., and E. Kenneally, "Applying 351 Ethical Principles to Information and Communication 352 Technology Research", October 2013, 353 . 356 [netem] Stephen, H., "Network emulation with NetEm", April 2005. 358 [PATHspider] 359 Learmonth, I., Trammell, B., Kuehlewind, M., and G. 360 Fairhurst, "PATHspider: A tool for active measurement of 361 path transparency", DOI 10.1145/2959424.2959441, July 362 2016, 363 . 365 [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for 366 Network Interconnect Devices", RFC 2544, 367 DOI 10.17487/RFC2544, March 1999, 368 . 370 [RFC6349] Constantine, B., Forget, G., Geib, R., and R. Schrage, 371 "Framework for TCP Throughput Testing", RFC 6349, 372 DOI 10.17487/RFC6349, August 2011, 373 . 375 [RFC6973] Cooper, A., Tschofenig, H., Aboba, B., Peterson, J., 376 Morris, J., Hansen, M., and R. Smith, "Privacy 377 Considerations for Internet Protocols", RFC 6973, July 378 2013, . 380 [Tor.2017-04-001] 381 Herm, K., "Privacy analysis of Tor's in-memory 382 statistics", Tor Tech Report 2017-04-001, April 2017, 383 . 386 [TorSafetyBoard] 387 Tor Project, "Tor Research Safety Board", 388 . 390 Author's Address 392 Iain R. Learmonth 393 Tor Project 395 Email: irl@torproject.org