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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 GG. Grover 5 Expires: January 13, 2022 Centre for Internet and Society 6 M. Knodel 7 Center for Democracy and Technology 8 July 12, 2021 10 Guidelines for Performing Safe Measurement on the Internet 11 draft-irtf-pearg-safe-internet-measurement-05 13 Abstract 15 Researchers from industry and academia often use Internet 16 measurements as part of their work. While these measurements can 17 give insight into the functioning and usage of the Internet, they can 18 come at the cost of user privacy. This document describes guidelines 19 for ensuring that such measurements can be carried out safely. 21 Note 23 Comments are solicited and should be addressed to the research 24 group's mailing list at pearg@irtf.org and/or the author(s). 26 The sources for this draft are at: 28 https://github.com/irl/draft-safe-internet-measurement 30 Status of This Memo 32 This Internet-Draft is submitted in full conformance with the 33 provisions of BCP 78 and BCP 79. 35 Internet-Drafts are working documents of the Internet Engineering 36 Task Force (IETF). Note that other groups may also distribute 37 working documents as Internet-Drafts. The list of current Internet- 38 Drafts is at https://datatracker.ietf.org/drafts/current/. 40 Internet-Drafts are draft documents valid for a maximum of six months 41 and may be updated, replaced, or obsoleted by other documents at any 42 time. It is inappropriate to use Internet-Drafts as reference 43 material or to cite them other than as "work in progress." 45 This Internet-Draft will expire on January 13, 2022. 47 Copyright Notice 49 Copyright (c) 2021 IETF Trust and the persons identified as the 50 document authors. All rights reserved. 52 This document is subject to BCP 78 and the IETF Trust's Legal 53 Provisions Relating to IETF Documents 54 (https://trustee.ietf.org/license-info) in effect on the date of 55 publication of this document. Please review these documents 56 carefully, as they describe your rights and restrictions with respect 57 to this document. 59 1. Introduction 61 Performing research using the Internet, as opposed to an isolated 62 testbed or simulation platform, means that experiments co-exist in a 63 space with other users. This document outlines guidelines for 64 academic and industry researchers that might use the Internet as part 65 of scientific experimentation to mitigate risks to the safety of 66 other users. 68 1.1. Scope of this document 70 These are guidelines for how to measure the Internet safely. When 71 performing research on a platform shared with live traffic from other 72 users, that research is considered safe if and only if other users 73 are protected from or unlikely to experience danger, risk, or injury, 74 now or in the future, due to the research. 76 Following the guidelines contained within this document is not a 77 substitute for any institutional ethics review process, although 78 these guidelines could help to inform that process. It is 79 particularly important for the growing area of research that includes 80 Internet measurement to better equip review boards to evaluate 81 Internet measurement methods [SIGCOMM], and we hope that this 82 document is part of that larger effort. 84 Similarly, these guidelines are not legal advice and local laws must 85 also be considered before starting any experiment that could have 86 adverse impacts on user safety. 88 The scope of this document is restricted to guidelines that mitigate 89 exposure to risks to Internet user safety when measuring properties 90 of the Internet: the network, its constiuent hosts and links, or its 91 users traffic. 93 For the purpose of this document, an Internet user is an individual 94 or organisation whose data is used in communications over the 95 Internet, most broadly, and those who use the Internet to communicate 96 or maintain Internet infrastructure. 98 1.2. Threat Model 100 A threat is a potential for a security violation, which exists when 101 there is a circumstance, capability, action, or event that could 102 breach security and cause harm [RFC4949]. Every Internet measurement 103 study has the potential to subject Internet users to threat actions, 104 or attacks. 106 Many of the threats to user safety occur from an instantiation (or 107 combination) of the following: 109 Surveillance: An attack whereby an Internet user's information is 110 collected. This type of attack covers not only data but also 111 metadata. 113 Inadequate protection of collected data: An attack where data, either 114 in flight or at rest, was not adequately protected from disclosure. 115 Failure to adequately protect data to the expectations of the user is 116 an attack even if it does not lead to another party gaining access to 117 the data. 119 Traffic generation: An attack whereby traffic is generated to 120 traverse the Internet. 122 Traffic modification: An attack whereby the Internet traffic of users 123 is modified. 125 Any conceivable Internet measurement study might be considered an 126 attack on an Internet user's safety. It is always necessary to 127 consider the best approach to mitigate the impact of measurements, 128 and to balance the risks of measurements against the benefits to 129 impacted users. 131 1.3. Measurement Studies 133 Internet measurement studies can be broadly categorized into two 134 groups: active measurements and passive measurements. Active 135 measurements generate or modify traffic while passive measurements 136 use surveillance of existing traffic. The type of measurement is not 137 truly binary and many studies will include both active and passive 138 components. The measurement of generated traffic may also lead to 139 insights into other users' traffic indirectly. 141 XXX On-path/off-path 142 XXX One ended/two ended 144 1.4. User Impact from Measurement Studies 146 Consequences of attacks 148 Breach of Privacy: data collection. This impact also covers the case 149 of an Internet user's data being shared beyond that which a user had 150 given consent for. 152 Impersonation: An attack where a user is impersonated during a 153 measurement. 155 XXX Legal 157 XXX Other Retribution 159 System corruption: An attack where generated or modified traffic 160 causes the corruption of a system. This attack covers cases where a 161 user's data may be lost or corrupted, and cases where a user's access 162 to a system may be affected. 164 XXX Data loss, corruption 166 XXX Denial of Service (by which self-censorship is covered) 168 XXX Emotional Trauma 170 2. Consent 172 Accountability and transparency are fundamentally related to consent. 173 As per the Menlo Report, "Accountability demands that research 174 methodology, ethical evaluations, data collected, and results 175 generated should be documented and made available responsibly in 176 accordance with balancing risks and benefits."[MenloReport] 178 XXX a user is best placed to balanced risks vs benefits themselves 180 In an ideal world, informed consent would be collected from all users 181 that may be placed at risk, no matter how small a risk, by an 182 experiment. In cases where it is practical to do so, this should be 183 done. 185 2.1. Informed Consent 187 For consent to be informed, all possible risks must be presented to 188 the users. The considerations in this document can be used to 189 provide a starting point although other risks may be present 190 depending on the nature of the measurements to be performed. 192 2.2. Informed Consent: Case Study 194 A researcher would like to use volunteer owned mobile devices to 195 collect information about local Internet censorship. Connections 196 will be made from the volunteer's device towards known or suspected 197 blocked webpages. 199 This experiment can carry substantial risk for the user depending on 200 the circumstances, from disciplinary action from their employer to 201 arrest or imprisonment. Fully informed consent ensures that any risk 202 that is being taken has been carefully considered by the volunteer 203 before proceeding. 205 2.3. Proxy Consent 207 In cases where it is not practical to collect informed consent from 208 all users of a shared network, it may be possible to obtain proxy 209 consent. Proxy consent may be given by a network operator or 210 employer that would be more familiar with the expectations of users 211 of a network than the researcher. 213 In some cases, a network operator or employer may have terms of 214 service that specifically allow for giving consent to 3rd parties to 215 perform certain experiments. 217 2.4. Proxy Consent: Case Study 219 A researcher would like to perform a packet capture to determine the 220 TCP options and their values used by all client devices on an 221 corporate wireless network. 223 The employer may already have terms of service laid out that allow 224 them to provide proxy consent for this experiment on behalf of the 225 employees (the users of the network). The purpose of the experiment 226 may affect whether or not they are able to provide this consent. For 227 example, to perform engineering work on the network then it may be 228 allowed, whereas academic research may not be covered. 230 2.5. Implied Consent 232 In larger scale measurements, even proxy consent collection may not 233 be practical. In this case, implied consent may be presumed from 234 users for some measurements. Consider that users of a network will 235 have certain expectations of privacy and those expectations may not 236 align with the privacy guarantees offered by the technologies they 237 are using. As a thought experiment, consider how users might respond 238 if asked for their informed consent for the measurements you'd like 239 to perform. 241 Implied consent should not be considered sufficient for any 242 experiment that may collect sensitive or personally identifying 243 information. If practical, attempt to obtain informed consent or 244 proxy consent from a sample of users to better understand the 245 expectations of other users. 247 2.6. Implied Consent: Case Study 1 249 A researcher would like to run a measurement campaign to determine 250 the maximum supported TLS version on popular web servers. 252 The operator of a web server that is exposed to the Internet hosting 253 a popular website would have the expectation that it may be included 254 in surveys that look at supported protocols or extensions but would 255 not expect that attempts be made to degrade the service with large 256 numbers of simultaneous connections. 258 2.7. Implied Consent: Case Study 2 260 A researcher would like to perform A/B testing for protocol feature 261 and how it affects web performance. They have created two versions 262 of their software and have instrumented both to report telemetry 263 back. These updates will be pushed to users at random by the 264 software's auto-update framework. The telemetry consists only of 265 performance metrics and does not contain any personally identifying 266 or sensitive information. 268 As users expect to receive automatic updates, the effect of changing 269 the behaviour of the software is already expected by the user. If 270 users have already been informed that data will be reported back to 271 the developers of the software, then again the addition of new 272 metrics would be expected. There are risks in pushing any new 273 software update, and the A/B testing technique can reduce the number 274 of users that may be adversely affected by a bad update. 276 The reduced impact should not be used as an excuse for pushing higher 277 risk updates, only updates that could be considered appropriate to 278 push to all users should be A/B tested. Likewise, not pushing the 279 new behaviour to any user should be considered appropriate if some 280 users are to remain with the old behavior. 282 In the event that something does go wrong with the update, it should 283 be easy for a user to discover that they have been part of an 284 experiment and roll back the change, allowing for explicit refusal of 285 consent to override the presumed implied consent. 287 3. Safety Considerations 289 3.1. Isolate risk with a dedicated testbed 291 Wherever possible, use a testbed. An isolated network means that 292 there are no other users sharing the infrastructure you are using for 293 your experiments. 295 When measuring performance, competing traffic can have negative 296 effects on the performance of your test traffic and so the testbed 297 approach can also produce more accurate and repeatable results than 298 experiments using the public Internet. 300 WAN link conditions can be emulated through artificial delays and/or 301 packet loss using a tool like [netem]. Competing traffic can also be 302 emulated using traffic generators. 304 3.2. Be respectful of other's infrastructure 306 If your experiment is designed to trigger a response from 307 infrastructure that is not your own, consider what the negative 308 consequences of that may be. At the very least your experiment will 309 consume bandwidth that may have to be paid for. 311 In more extreme circumstances, you could cause traffic to be 312 generated that causes legal trouble for the owner of that 313 infrastructure. The Internet is a global network crossing many legal 314 jurisdictions and so what may be legal for you is not necessarily 315 legal for everyone. 317 If you are sending a lot of traffic quickly, or otherwise generally 318 deviate from typical client behaviour, a network may identify this as 319 an attack which means that you will not be collecting results that 320 are representative of what a typical client would see. 322 3.2.1. Maintain a "Do Not Scan" list 324 When performing active measurements on a shared network, maintain a 325 list of hosts that you will never scan regardless of whether they 326 appear in your target lists. When developing tools for performing 327 active measurement, or traffic generation for use in a larger 328 measurement system, ensure that the tool will support the use of a 329 "Do Not Scan" list. 331 If complaints are made that request you do not generate traffic 332 towards a host or network, you must add that host or network to your 333 "Do Not Scan" list, even if no explanation is given or the request is 334 automated. 336 You may ask the requester for their reasoning if it would be useful 337 to your experiment. This can also be an opportunity to explain your 338 research and offer to share any results that may be of interest. If 339 you plan to share the reasoning when publishing your measurement 340 results, e.g. in an academic paper, you must seek consent for this 341 from the requester. 343 Be aware that in publishing your measurement results, it may be 344 possible to infer your "Do Not Scan" list from those results. For 345 example, if you measured a well-known list of popular websites then 346 it would be possible to correlate the results with that list to 347 determine which are missing. 349 3.3. Data Minimization 351 When collecting, using, disclosing, and storing data from a 352 measurement, use only the minimal data necessary to perform a task. 353 Reducing the amount of data reduces the amount of data that can be 354 misused or leaked. 356 When deciding on the data to collect, assume that any data collected 357 might be disclosed. There are many ways that this could happen, 358 through operation security mistakes or compulsion by a judicial 359 system. 361 When directly instrumenting a protocol to provide metrics to a 362 passive observer, see section 6.1 of RFC6973 [RFC6973] for data 363 minimalization considerations specific to this use case. 365 3.3.1. Discarding Data 367 XXX: Discard data that is not required to perform the task. 369 When performing active measurements be sure to only capture traffic 370 that you have generated. Traffic may be identified by IP ranges or 371 by some token that is unlikely to be used by other users. 373 Again, this can help to improve the accuracy and repeatability of 374 your experiment. [RFC2544], for performance benchmarking, requires 375 that any frames received that were not part of the test traffic are 376 discarded and not counted in the results. 378 3.3.2. Masking Data 380 XXX: Mask data that is not required to perform the task. 381 Particularly useful for content of traffic to indicate that either a 382 particular class of content existed or did not exist, or the length 383 of the content, but not recording the content itself. Can also 384 replace content with tokens, or encrypt. 386 3.3.3. Reduce Accuracy 388 XXX: Binning, categorizing, geoip, noise. 390 3.3.4. Data Aggregation 392 When collecting data, consider if the granularity can be limited by 393 using bins or adding noise. XXX: Differential privacy. 395 XXX: Do this at the source, definitely do it before you write to 396 disk. 398 [Tor.2017-04-001] presents a case-study on the in-memory statistics 399 in the software used by the Tor network, as an example. 401 4. Risk Analysis 403 The benefits should outweigh the risks. Consider auxiliary data 404 (e.g. third-party data sets) when assessing the risks. 406 5. Security Considerations 408 Take reasonable security precautions, e.g. about who has access to 409 your data sets or experimental systems. 411 6. IANA Considerations 413 This document has no actions for IANA. 415 7. Acknowledgements 417 Many of these considerations are based on those from the 418 [TorSafetyBoard] adapted and generalised to be applied to Internet 419 research. 421 Other considerations are taken from the Menlo Report [MenloReport] 422 and its companion document [MenloReportCompanion]. 424 8. Informative References 426 [MenloReport] 427 Dittrich, D. and E. Kenneally, "The Menlo Report: Ethical 428 Principles Guiding Information and Communication 429 Technology Research", August 2012, 430 . 433 [MenloReportCompanion] 434 Bailey, M., Dittrich, D., and E. Kenneally, "Applying 435 Ethical Principles to Information and Communication 436 Technology Research", October 2013, 437 . 440 [netem] Stephen, H., "Network emulation with NetEm", April 2005. 442 [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for 443 Network Interconnect Devices", RFC 2544, 444 DOI 10.17487/RFC2544, March 1999, 445 . 447 [RFC4949] Shirey, R., "Internet Security Glossary, Version 2", 448 August 2007, . 450 [RFC6973] Cooper, A., Tschofenig, H., Aboba, B., Peterson, J., 451 Morris, J., Hansen, M., and R. Smith, "Privacy 452 Considerations for Internet Protocols", RFC 6973, July 453 2013, . 455 [SIGCOMM] Jones, B., Ensafi, R., Feamster, N., Paxson, V., and N. 456 Weaver, "Ethical Concerns for Censorship Measurement", 457 August 2015, 458 . 461 [Tor.2017-04-001] 462 Herm, K., "Privacy analysis of Tor's in-memory 463 statistics", Tor Tech Report 2017-04-001, April 2017, 464 . 467 [TorSafetyBoard] 468 Tor Project, "Tor Research Safety Board", 469 . 471 Authors' Addresses 473 Iain R. Learmonth 474 Tor Project 476 Email: irl@torproject.org 478 Gurshabad Grover 479 Centre for Internet and Society 481 Email: gurshabad@cis-india.org 483 Mallory Knodel 484 Center for Democracy and Technology 486 Email: mknodel@cdt.org