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rfc:rfc7624

Internet Architecture Board (IAB) R. Barnes Request for Comments: 7624 B. Schneier Category: Informational C. Jennings ISSN: 2070-1721 T. Hardie

                                                           B. Trammell
                                                            C. Huitema
                                                           D. Borkmann
                                                           August 2015
       Confidentiality in the Face of Pervasive Surveillance:
                A Threat Model and Problem Statement

Abstract

 Since the initial revelations of pervasive surveillance in 2013,
 several classes of attacks on Internet communications have been
 discovered.  In this document, we develop a threat model that
 describes these attacks on Internet confidentiality.  We assume an
 attacker that is interested in undetected, indiscriminate
 eavesdropping.  The threat model is based on published, verified
 attacks.

Status of This Memo

 This document is not an Internet Standards Track specification; it is
 published for informational purposes.
 This document is a product of the Internet Architecture Board (IAB)
 and represents information that the IAB has deemed valuable to
 provide for permanent record.  It represents the consensus of the
 Internet Architecture Board (IAB).  Documents approved for
 publication by the IAB are not a candidate for any level of Internet
 Standard; see Section 2 of RFC 5741.
 Information about the current status of this document, any errata,
 and how to provide feedback on it may be obtained at
 http://www.rfc-editor.org/info/rfc7624.

Barnes, et al. Informational [Page 1] RFC 7624 Confidentiality Threat Model August 2015

Copyright Notice

 Copyright (c) 2015 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
 (http://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  . . . . . . . . . . . . . . . . . . . . . . . .   3
 2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
 3.  An Idealized Passive Pervasive Attacker . . . . . . . . . . .   5
   3.1.  Information Subject to Direct Observation . . . . . . . .   6
   3.2.  Information Useful for Inference  . . . . . . . . . . . .   6
   3.3.  An Illustration of an Ideal Passive Pervasive Attack  . .   7
     3.3.1.  Analysis of IP Headers  . . . . . . . . . . . . . . .   7
     3.3.2.  Correlation of IP Addresses to User Identities  . . .   8
     3.3.3.  Monitoring Messaging Clients for IP Address
             Correlation . . . . . . . . . . . . . . . . . . . . .   9
     3.3.4.  Retrieving IP Addresses from Mail Headers . . . . . .   9
     3.3.5.  Tracking Address Usage with Web Cookies . . . . . . .  10
     3.3.6.  Graph-Based Approaches to Address Correlation . . . .  10
     3.3.7.  Tracking of Link-Layer Identifiers  . . . . . . . . .  10
 4.  Reported Instances of Large-Scale Attacks . . . . . . . . . .  11
 5.  Threat Model  . . . . . . . . . . . . . . . . . . . . . . . .  13
   5.1.  Attacker Capabilities . . . . . . . . . . . . . . . . . .  14
   5.2.  Attacker Costs  . . . . . . . . . . . . . . . . . . . . .  17
 6.  Security Considerations . . . . . . . . . . . . . . . . . . .  19
 7.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  20
   7.1.  Normative References  . . . . . . . . . . . . . . . . . .  20
   7.2.  Informative References  . . . . . . . . . . . . . . . . .  20
 IAB Members at the Time of Approval . . . . . . . . . . . . . . .  23
 Acknowledgements  . . . . . . . . . . . . . . . . . . . . . . . .  24
 Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

Barnes, et al. Informational [Page 2] RFC 7624 Confidentiality Threat Model August 2015

1. Introduction

 Starting in June 2013, documents released to the press by Edward
 Snowden have revealed several operations undertaken by intelligence
 agencies to exploit Internet communications for intelligence
 purposes.  These attacks were largely based on protocol
 vulnerabilities that were already known to exist.  The attacks were
 nonetheless striking in their pervasive nature, in terms of both the
 volume of Internet traffic targeted and the diversity of attack
 techniques employed.
 To ensure that the Internet can be trusted by users, it is necessary
 for the Internet technical community to address the vulnerabilities
 exploited in these attacks [RFC7258].  The goal of this document is
 to describe more precisely the threats posed by these pervasive
 attacks, and based on those threats, lay out the problems that need
 to be solved in order to secure the Internet in the face of those
 threats.
 The remainder of this document is structured as follows.  In
 Section 3, we describe an idealized passive pervasive attacker, one
 which could completely undetectably compromise communications at
 Internet scale.  In Section 4, we provide a brief summary of some
 attacks that have been disclosed, and use these to expand the assumed
 capabilities of our idealized attacker.  Note that we do not attempt
 to describe all possible attacks, but focus on those that result in
 undetected eavesdropping.  Section 5 describes a threat model based
 on these attacks, focusing on classes of attack that have not been a
 focus of Internet engineering to date.

2. Terminology

 This document makes extensive use of standard security and privacy
 terminology; see [RFC4949] and [RFC6973].  Terms used from [RFC6973]
 include Eavesdropper, Observer, Initiator, Intermediary, Recipient,
 Attack (in a privacy context), Correlation, Fingerprint, Traffic
 Analysis, and Identifiability (and related terms).  In addition, we
 use a few terms that are specific to the attacks discussed in this
 document.  Note especially that "passive" and "active" below do not
 refer to the effort used to mount the attack; a "passive attack" is
 any attack that accesses a flow but does not modify it, while an
 "active attack" is any attack that modifies a flow.  Some passive
 attacks involve active interception and modifications of devices,
 rather than simple access to the medium.  The introduced terms are:

Barnes, et al. Informational [Page 3] RFC 7624 Confidentiality Threat Model August 2015

 Pervasive Attack:  An attack on Internet communications that makes
    use of access at a large number of points in the network, or
    otherwise provides the attacker with access to a large amount of
    Internet traffic; see [RFC7258].
 Passive Pervasive Attack:  An eavesdropping attack undertaken by a
    pervasive attacker, in which the packets in a traffic stream
    between two endpoints are intercepted, but in which the attacker
    does not modify the packets in the traffic stream between two
    endpoints, modify the treatment of packets in the traffic stream
    (e.g., delay, routing), or add or remove packets in the traffic
    stream.  Passive pervasive attacks are undetectable from the
    endpoints.  Equivalent to passive wiretapping as defined in
    [RFC4949]; we use an alternate term here since the methods
    employed are wider than those implied by the word "wiretapping",
    including the active compromise of intermediate systems.
 Active Pervasive Attack:  An attack that is undertaken by a pervasive
    attacker and, in addition to the elements of a passive pervasive
    attack, also includes modification, addition, or removal of
    packets in a traffic stream, or modification of treatment of
    packets in the traffic stream.  Active pervasive attacks provide
    more capabilities to the attacker at the risk of possible
    detection at the endpoints.  Equivalent to active wiretapping as
    defined in [RFC4949].
 Observation:  Information collected directly from communications by
    an eavesdropper or observer.  For example, the knowledge that
    <alice@example.com> sent a message to <bob@example.com> via SMTP
    taken from the headers of an observed SMTP message would be an
    observation.
 Inference:  Information derived from analysis of information
    collected directly from communications by an eavesdropper or
    observer.  For example, the knowledge that a given web page was
    accessed by a given IP address, by comparing the size in octets of
    measured network flow records to fingerprints derived from known
    sizes of linked resources on the web servers involved, would be an
    inference.
 Collaborator:  An entity that is a legitimate participant in a
    communication, and provides information about that communication
    to an attacker.  Collaborators may either deliberately or
    unwittingly cooperate with the attacker, in the latter case
    because the attacker has subverted the collaborator through
    technical, social, or other means.

Barnes, et al. Informational [Page 4] RFC 7624 Confidentiality Threat Model August 2015

 Key Exfiltration:  The transmission of cryptographic keying material
    for an encrypted communication from a collaborator, deliberately
    or unwittingly, to an attacker.
 Content Exfiltration:  The transmission of the content of a
    communication from a collaborator, deliberately or unwittingly, to
    an attacker

3. An Idealized Passive Pervasive Attacker

 In considering the threat posed by pervasive surveillance, we begin
 by defining an idealized passive pervasive attacker.  While this
 attacker is less capable than those that we now know to have
 compromised the Internet from press reports, as elaborated in
 Section 4, it does set a lower bound on the capabilities of an
 attacker interested in indiscriminate passive surveillance while
 interested in remaining undetectable.  We note that, prior to the
 Snowden revelations in 2013, the assumptions of attacker capability
 presented here would be considered on the border of paranoia outside
 the network security community.
 Our idealized attacker is an indiscriminate eavesdropper that is on
 an Internet-attached computer network and:
 o  can observe every packet of all communications at any hop in any
    network path between an initiator and a recipient;
 o  can observe data at rest in any intermediate system between the
    endpoints controlled by the initiator and recipient; and
 o  can share information with other such attackers; but
 o  takes no other action with respect to these communications (i.e.,
    blocking, modification, injection, etc.).
 The techniques available to our ideal attacker are direct observation
 and inference.  Direct observation involves taking information
 directly from eavesdropped communications, such as URLs identifying
 content or email addresses identifying individuals from application-
 layer headers.  Inference, on the other hand, involves analyzing
 observed information to derive new information, such as searching for
 application or behavioral fingerprints in observed traffic to derive
 information about the observed individual.  The use of encryption is
 generally sufficient to provide confidentiality by preventing direct
 observation of content, assuming of course, uncompromised encryption
 implementations and cryptographic keying material.  However,
 encryption provides less complete protection against inference,

Barnes, et al. Informational [Page 5] RFC 7624 Confidentiality Threat Model August 2015

 especially inferences based only on plaintext portions of
 communications, such as IP and TCP headers for TLS-protected traffic
 [RFC5246].

3.1. Information Subject to Direct Observation

 Protocols that do not encrypt their payload make the entire content
 of the communication available to the idealized attacker along their
 path.  Following the advice in [RFC3365], most such protocols have a
 secure variant that encrypts the payload for confidentiality, and
 these secure variants are seeing ever-wider deployment.  A noteworthy
 exception is DNS [RFC1035], as DNSSEC [RFC4033] does not have
 confidentiality as a requirement.
 This implies that, in the absence of changes to the protocol as
 presently under development in the IETF's DNS Private Exchange
 (DPRIVE) working group [DPRIVE], all DNS queries and answers
 generated by the activities of any protocol are available to the
 attacker.
 When store-and-forward protocols are used (e.g., SMTP [RFC5321]),
 intermediaries leave this data subject to observation by an attacker
 that has compromised these intermediaries, unless the data is
 encrypted end-to-end by the application-layer protocol or the
 implementation uses an encrypted store for this data.

3.2. Information Useful for Inference

 Inference is information extracted from later analysis of an observed
 or eavesdropped communication, and/or correlation of observed or
 eavesdropped information with information available from other
 sources.  Indeed, most useful inference performed by the attacker
 falls under the rubric of correlation.  The simplest example of this
 is the observation of DNS queries and answers from and to a source
 and correlating those with IP addresses with which that source
 communicates.  This can give access to information otherwise not
 available from encrypted application payloads (e.g., the "Host:"
 HTTP/1.1 request header when HTTP is used with TLS).
 Protocols that encrypt their payload using an application- or
 transport-layer encryption scheme (e.g., TLS) still expose all the
 information in their network- and transport-layer headers to the
 attacker, including source and destination addresses and ports.
 IPsec Encapsulating Security Payload (ESP) [RFC4303] further encrypts
 the transport-layer headers but still leaves IP address information
 unencrypted; in tunnel mode, these addresses correspond to the tunnel
 endpoints.  Features of the security protocols themselves, e.g., the
 TLS session identifier, may leak information that can be used for

Barnes, et al. Informational [Page 6] RFC 7624 Confidentiality Threat Model August 2015

 correlation and inference.  While this information is much less
 semantically rich than the application payload, it can still be
 useful for inferring an individual's activities.
 Inference can also leverage information obtained from sources other
 than direct traffic observation.  Geolocation databases, for example,
 have been developed that map IP addresses to a location, in order to
 provide location-aware services such as targeted advertising.  This
 location information is often of sufficient resolution that it can be
 used to draw further inferences toward identifying or profiling an
 individual.
 Social media provide another source of more or less publicly
 accessible information.  This information can be extremely
 semantically rich, including information about an individual's
 location, associations with other individuals and groups, and
 activities.  Further, this information is generally contributed and
 curated voluntarily by the individuals themselves: it represents
 information that the individuals are not necessarily interested in
 protecting for privacy reasons.  However, correlation of this social
 networking data with information available from direct observation of
 network traffic allows the creation of a much richer picture of an
 individual's activities than either alone.
 We note with some alarm that there is little that can be done at
 protocol design time to limit such correlation by the attacker, and
 that the existence of such data sources in many cases greatly
 complicates the problem of protecting privacy by hardening protocols
 alone.

3.3. An Illustration of an Ideal Passive Pervasive Attack

 To illustrate how capable the idealized attacker is even given its
 limitations, we explore the non-anonymity of encrypted IP traffic in
 this section.  Here, we examine in detail some inference techniques
 for associating a set of addresses with an individual, in order to
 illustrate the difficulty of defending communications against our
 idealized attacker.  Here, the basic problem is that information
 radiated even from protocols that have no obvious connection with
 personal data can be correlated with other information that can paint
 a very rich behavioral picture; it only takes one unprotected link in
 the chain to associate with an identity.

3.3.1. Analysis of IP Headers

 Internet traffic can be monitored by tapping Internet links or by
 installing monitoring tools in Internet routers.  Of course, a single
 link or a single router only provides access to a fraction of the

Barnes, et al. Informational [Page 7] RFC 7624 Confidentiality Threat Model August 2015

 global Internet traffic.  However, monitoring a number of high-
 capacity links or a set of routers placed at strategic locations
 provides access to a good sampling of Internet traffic.
 Tools like the IP Flow Information Export (IPFIX) Protocol [RFC7011]
 allow administrators to acquire statistics about sequences of packets
 with some common properties that pass through a network device.  The
 most common set of properties used in flow measurement is the "five-
 tuple" of source and destination addresses, protocol type, and source
 and destination ports.  These statistics are commonly used for
 network engineering but could certainly be used for other purposes.
 Let's assume for a moment that IP addresses can be correlated to
 specific services or specific users.  Analysis of the sequences of
 packets will quickly reveal which users use what services, and also
 which users engage in peer-to-peer connections with other users.
 Analysis of traffic variations over time can be used to detect
 increased activity by particular users or, in the case of peer-to-
 peer connections, increased activity within groups of users.

3.3.2. Correlation of IP Addresses to User Identities

 The correlation of IP addresses with specific users can be done in
 various ways.  For example, tools like reverse DNS lookup can be used
 to retrieve the DNS names of servers.  Since the addresses of servers
 tend to be quite stable and since servers are relatively less
 numerous than users, an attacker could easily maintain its own copy
 of the DNS for well-known or popular servers to accelerate such
 lookups.
 On the other hand, the reverse lookup of IP addresses of users is
 generally less informative.  For example, a lookup of the address
 currently used by one author's home network returns a name of the
 form "c-192-000-002-033.hsd1.wa.comcast.net".  This particular type
 of reverse DNS lookup generally reveals only coarse-grained location
 or provider information, equivalent to that available from
 geolocation databases.
 In many jurisdictions, Internet Service Providers (ISPs) are required
 to provide identification on a case-by-case basis of the "owner" of a
 specific IP address for law enforcement purposes.  This is a
 reasonably expedient process for targeted investigations, but
 pervasive surveillance requires something more efficient.  This
 provides an incentive for the attacker to secure the cooperation of
 the ISP in order to automate this correlation.

Barnes, et al. Informational [Page 8] RFC 7624 Confidentiality Threat Model August 2015

3.3.3. Monitoring Messaging Clients for IP Address Correlation

 Even if the ISP does not cooperate, user identity can often be
 obtained via inference.  POP3 [RFC1939] and IMAP [RFC3501] are used
 to retrieve mail from mail servers, while a variant of SMTP is used
 to submit messages through mail servers.  IMAP connections originate
 from the client, and typically start with an authentication exchange
 in which the client proves its identity by answering a password
 challenge.  The same holds for the SIP protocol [RFC3261] and many
 instant messaging services operating over the Internet using
 proprietary protocols.
 The username is directly observable if any of these protocols operate
 in cleartext; the username can then be directly associated with the
 source address.

3.3.4. Retrieving IP Addresses from Mail Headers

 SMTP [RFC5321] requires that each successive SMTP relay adds a
 "Received" header to the mail headers.  The purpose of these headers
 is to enable audit of mail transmission, and perhaps to distinguish
 between regular mail and spam.  Here is an extract from the headers
 of a message recently received from the perpass mailing list:
 Received: from 192-000-002-044.zone13.example.org (HELO
 ?192.168.1.100?) (xxx.xxx.xxx.xxx) by lvps192-000-002-219.example.net
 with ESMTPSA (DHE-RSA-AES256-SHA encrypted, authenticated); 27 Oct
 2013 21:47:14 +0100 Message-ID: <526D7BD2.7070908@example.org> Date:
 Sun, 27 Oct 2013 20:47:14 +0000 From: Some One <some.one@example.org>
 This is the first "Received" header attached to the message by the
 first SMTP relay; for privacy reasons, the field values have been
 anonymized.  We learn here that the message was submitted by "Some
 One" on October 27, from a host behind a NAT (192.168.1.100)
 [RFC1918] that used the IP address 192.0.2.44.  The information
 remained in the message and is accessible by all recipients of the
 perpass mailing list, or indeed by any attacker that sees at least
 one copy of the message.
 An attacker that can observe sufficient email traffic can regularly
 update the mapping between public IP addresses and individual email
 identities.  Even if the SMTP traffic was encrypted on submission and
 relaying, the attacker can still receive a copy of public mailing
 lists like perpass.

Barnes, et al. Informational [Page 9] RFC 7624 Confidentiality Threat Model August 2015

3.3.5. Tracking Address Usage with Web Cookies

 Many web sites only encrypt a small fraction of their transactions.
 A popular pattern is to use HTTPS for the login information, and then
 use a "cookie" to associate following cleartext transactions with the
 user's identity.  Cookies are also used by various advertisement
 services to quickly identify the users and serve them with
 "personalized" advertisements.  Such cookies are particularly useful
 if the advertisement services want to keep tracking the user across
 multiple sessions that may use different IP addresses.
 As cookies are sent in cleartext, an attacker can build a database
 that associates cookies to IP addresses for non-HTTPS traffic.  If
 the IP address is already identified, the cookie can be linked to the
 user identify.  After that, if the same cookie appears on a new IP
 address, the new IP address can be immediately associated with the
 predetermined identity.

3.3.6. Graph-Based Approaches to Address Correlation

 An attacker can track traffic from an IP address not yet associated
 with an individual to various public services (e.g., web sites, mail
 servers, game servers) and exploit patterns in the observed traffic
 to correlate this address with other addresses that show similar
 patterns.  For example, any two addresses that show connections to
 the same IMAP or webmail services, the same set of favorite web
 sites, and game servers at similar times of day may be associated
 with the same individual.  Correlated addresses can then be tied to
 an individual through one of the techniques above, walking the
 "network graph" to expand the set of attributable traffic.

3.3.7. Tracking of Link-Layer Identifiers

 Moving back down the stack, technologies like Ethernet or Wi-Fi use
 MAC (Media Access Control) addresses to identify link-level
 destinations.  MAC addresses assigned according to IEEE 802 standards
 are globally unique identifiers for the device.  If the link is
 publicly accessible, an attacker can eavesdrop and perform tracking.
 For example, the attacker can track the wireless traffic at publicly
 accessible Wi-Fi networks.  Simple devices can monitor the traffic
 and reveal which MAC addresses are present.  Also, devices do not
 need to be connected to a network to expose link-layer identifiers.
 Active service discovery always discloses the MAC address of the
 user, and sometimes the Service Set Identifiers (SSIDs) of previously
 visited networks.  For instance, certain techniques such as the use
 of "hidden SSIDs" require the mobile device to broadcast the network
 identifier together with the device identifier.  This combination can
 further expose the user to inference attacks, as more information can

Barnes, et al. Informational [Page 10] RFC 7624 Confidentiality Threat Model August 2015

 be derived from the combination of MAC address, SSID being probed,
 time, and current location.  For example, a user actively probing for
 a semi-unique SSID on a flight out of a certain city can imply that
 the user is no longer at the physical location of the corresponding
 AP.  Given that large-scale databases of the MAC addresses of
 wireless access points for geolocation purposes have been known to
 exist for some time, the attacker could easily build a database that
 maps link-layer identifiers and time with device or user identities,
 and use it to track the movement of devices and of their owners.  On
 the other hand, if the network does not use some form of Wi-Fi
 encryption, or if the attacker can access the decrypted traffic, the
 analysis will also provide the correlation between link-layer
 identifiers such as MAC addresses and IP addresses.  Additional
 monitoring using techniques exposed in the previous sections will
 reveal the correlation between MAC addresses, IP addresses, and user
 identity.  For instance, similarly to the use of web cookies, MAC
 addresses provide identity information that can be used to associate
 a user to different IP addresses.

4. Reported Instances of Large-Scale Attacks

 The situation in reality is more bleak than that suggested by an
 analysis of our idealized attacker.  Through revelations of sensitive
 documents in several media outlets, the Internet community has been
 made aware of several intelligence activities conducted by US and UK
 national intelligence agencies, particularly the US National Security
 Agency (NSA) and the UK Government Communications Headquarters
 (GCHQ).  These documents have revealed methods that these agencies
 use to attack Internet applications and obtain sensitive user
 information.  There is little reason to suppose that only the US or
 UK governments are involved in these sorts of activities; the
 examples are just ones that were disclosed.  We note that these
 reports are primarily useful as an illustration of the types of
 capabilities fielded by pervasive attackers as of the date of the
 Snowden leaks in 2013.
 First, they confirm the deployment of large-scale passive collection
 of Internet traffic, which confirms the existence of pervasive
 passive attackers with at least the capabilities of our idealized
 attacker.  For example, as described in [pass1], [pass2], [pass3],
 and [pass4]:
 o  NSA's XKEYSCORE system accesses data from multiple access points
    and searches for "selectors" such as email addresses, at the scale
    of tens of terabytes of data per day.
 o  GCHQ's Tempora system appears to have access to around 1,500 major
    cables passing through the UK.

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 o  NSA's MUSCULAR program has tapped cables between data centers
    belonging to major service providers.
 o  Several programs appear to perform wide-scale collection of
    cookies in web traffic and location data from location-aware
    portable devices such as smartphones.
 However, the capabilities described by these reports go beyond those
 of our idealized attacker.  They include the compromise of
 cryptographic protocols, including decryption of TLS-protected
 Internet sessions [dec1] [dec2] [dec3].  For example, the NSA BULLRUN
 project worked to undermine encryption through multiple approaches,
 including covert modifications to cryptographic software on end
 systems.
 Reported capabilities include the direct compromise of intermediate
 systems and arrangements with service providers for bulk data and
 metadata access [dir1] [dir2] [dir3], bypassing the need to capture
 traffic on the wire.  For example, the NSA PRISM program provides the
 agency with access to many types of user data (e.g., email, chat,
 VoIP).
 The reported capabilities also include elements of active pervasive
 attack, including:
 o  Insertion of devices as a man-in-the-middle of Internet
    transactions [TOR1] [TOR2].  For example, NSA's QUANTUM system
    appears to use several different techniques to hijack HTTP
    connections, ranging from DNS response injection to HTTP 302
    redirects.
 o  Use of implants on end systems to undermine security and anonymity
    features [dec2] [TOR1] [TOR2].  For example, QUANTUM is used to
    direct users to a FOXACID server, which in turn delivers an
    implant to compromise browsers of Tor users.
 o  Use of implants on network elements from many major equipment
    providers, including Cisco, Juniper, Huawei, Dell, and HP, as
    provided by the NSA's Advanced Network Technology group
    [spiegel1].
 o  Use of botnet-scale collections of compromised hosts [spiegel2].
 The scale of the compromise extends beyond the network to include
 subversion of the technical standards process itself.  For example,
 there is suspicion that NSA modifications to the DUAL_EC_DRBG random
 number generator (RNG) were made to ensure that keys generated using
 that generator could be predicted by NSA.  This RNG was made part of

Barnes, et al. Informational [Page 12] RFC 7624 Confidentiality Threat Model August 2015

 NIST's SP 800-90A, for which NIST acknowledges the NSA's assistance.
 There have also been reports that the NSA paid RSA Security for a
 related contract with the result that the curve became the default in
 the RSA BSAFE product line.
 We use the term "pervasive attack" [RFC7258] to collectively describe
 these operations.  The term "pervasive" is used because the attacks
 are designed to indiscriminately gather as much data as possible and
 to apply selective analysis on targets after the fact.  This means
 that all, or nearly all, Internet communications are targets for
 these attacks.  To achieve this scale, the attacks are physically
 pervasive; they affect a large number of Internet communications.
 They are pervasive in content, consuming and exploiting any
 information revealed by the protocol.  And they are pervasive in
 technology, exploiting many different vulnerabilities in many
 different protocols.
 Again, it's important to note that, although the attacks mentioned
 above were executed by the NSA and GCHQ, there are many other
 organizations that can mount pervasive surveillance attacks.  Because
 of the resources required to achieve pervasive scale, these attacks
 are most commonly undertaken by nation-state actors.  For example,
 the Chinese Internet filtering system known as the "Great Firewall of
 China" uses several techniques that are similar to the QUANTUM
 program and that have a high degree of pervasiveness with regard to
 the Internet in China.  Therefore, legal restrictions in any one
 jurisdiction on pervasive monitoring activities cannot eliminate the
 risk of pervasive attack to the Internet as a whole.

5. Threat Model

 Given these disclosures, we must consider a broader threat model.
 Pervasive surveillance aims to collect information across a large
 number of Internet communications, analyzing the collected
 communications to identify information of interest within individual
 communications, or inferring information from correlated
 communications.  This analysis sometimes benefits from decryption of
 encrypted communications and deanonymization of anonymized
 communications.  As a result, these attackers desire both access to
 the bulk of Internet traffic and to the keying material required to
 decrypt any traffic that has been encrypted.  Even if keys are not
 available, note that the presence of a communication and the fact
 that it is encrypted may both be inputs to an analysis, even if the
 attacker cannot decrypt the communication.

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 The attacks listed above highlight new avenues both for access to
 traffic and for access to relevant encryption keys.  They further
 indicate that the scale of surveillance is sufficient to provide a
 general capability to cross-correlate communications, a threat not
 previously thought to be relevant at the scale of the Internet.

5.1. Attacker Capabilities

  +--------------------------+-------------------------------------+
  | Attack Class             | Capability                          |
  +--------------------------+-------------------------------------+
  | Passive observation      | Directly capture data in transit    |
  |                          |                                     |
  | Passive inference        | Infer from reduced/encrypted data   |
  |                          |                                     |
  | Active                   | Manipulate / inject data in transit |
  |                          |                                     |
  | Static key exfiltration  | Obtain key material once / rarely   |
  |                          |                                     |
  | Dynamic key exfiltration | Obtain per-session key material     |
  |                          |                                     |
  | Content exfiltration     | Access data at rest                 |
  +--------------------------+-------------------------------------+
 Security analyses of Internet protocols commonly consider two classes
 of attacker: passive pervasive attackers, who can simply listen in on
 communications as they transit the network, and active pervasive
 attackers, who can modify or delete packets in addition to simply
 collecting them.
 In the context of pervasive passive surveillance, these attacks take
 on an even greater significance.  In the past, these attackers were
 often assumed to operate near the edge of the network, where attacks
 can be simpler.  For example, in some LANs, it is simple for any node
 to engage in passive listening to other nodes' traffic or inject
 packets to accomplish active pervasive attacks.  However, as we now
 know, both passive and active pervasive attacks are undertaken by
 pervasive attackers closer to the core of the network, greatly
 expanding the scope and capability of the attacker.
 Eavesdropping and observation at a larger scale make passive
 inference attacks easier to carry out: a passive pervasive attacker
 with access to a large portion of the Internet can analyze collected
 traffic to create a much more detailed view of individual behavior
 than an attacker that collects at a single point.  Even the usual
 claim that encryption defeats passive pervasive attackers is
 weakened, since a pervasive flow access attacker can infer
 relationships from correlations over large numbers of sessions, e.g.,

Barnes, et al. Informational [Page 14] RFC 7624 Confidentiality Threat Model August 2015

 pairing encrypted sessions with unencrypted sessions from the same
 host, or performing traffic fingerprinting between known and unknown
 encrypted sessions.  Reports on the NSA XKEYSCORE system would
 indicate it is an example of such an attacker.
 An active pervasive attacker likewise has capabilities beyond those
 of a localized active attacker.  Flow modification attacks are often
 limited by network topology, for example, by a requirement that the
 attacker be able to see a targeted session as well as inject packets
 into it.  A pervasive flow modification attacker with access at
 multiple points within the core of the Internet is able to overcome
 these topological limitations and perform attacks over a much broader
 scope.  Being positioned in the core of the network rather than the
 edge can also enable an active pervasive attacker to reroute targeted
 traffic, amplifying the ability to perform both eavesdropping and
 traffic injection.  Active pervasive attackers can also benefit from
 passive pervasive collection to identify vulnerable hosts.
 While not directly related to pervasiveness, attackers that are in a
 position to mount an active pervasive attack are also often in a
 position to subvert authentication, a traditional protection against
 such attacks.  Authentication in the Internet is often achieved via
 trusted third-party authorities such as the Certificate Authorities
 (CAs) that provide web sites with authentication credentials.  An
 attacker with sufficient resources may also be able to induce an
 authority to grant credentials for an identity of the attacker's
 choosing.  If the parties to a communication will trust multiple
 authorities to certify a specific identity, this attack may be
 mounted by suborning any one of the authorities (the proverbial
 "weakest link").  Subversion of authorities in this way can allow an
 active attack to succeed in spite of an authentication check.
 Beyond these three classes (observation, inference, and active),
 reports on the BULLRUN effort to defeat encryption and the PRISM
 effort to obtain data from service providers suggest three more
 classes of attack:
 o  Static key exfiltration
 o  Dynamic key exfiltration
 o  Content exfiltration
 These attacks all rely on a collaborator providing the attacker with
 some information, either keys or data.  These attacks have not
 traditionally been considered in scope for the Security
 Considerations sections of IETF protocols, as they occur outside the
 protocol.

Barnes, et al. Informational [Page 15] RFC 7624 Confidentiality Threat Model August 2015

 The term "key exfiltration" refers to the transfer of keying material
 for an encrypted communication from the collaborator to the attacker.
 By "static", we mean that the transfer of keys happens once or rarely
 and that the transferred key is typically long-lived.  For example,
 this case would cover a web site operator that provides the private
 key corresponding to its HTTPS certificate to an intelligence agency.
 "Dynamic" key exfiltration, by contrast, refers to attacks in which
 the collaborator delivers keying material to the attacker frequently,
 e.g., on a per-session basis.  This does not necessarily imply
 frequent communications with the attacker; the transfer of keying
 material may be virtual.  For example, if an endpoint were modified
 in such a way that the attacker could predict the state of its
 pseudorandom number generator, then the attacker would be able to
 derive per-session keys even without per-session communications.
 Finally, content exfiltration is the attack in which the collaborator
 simply provides the attacker with the desired data or metadata.
 Unlike the key exfiltration cases, this attack does not require the
 attacker to capture the desired data as it flows through the network.
 The exfiltration is of data at rest, rather than data in transit.
 This increases the scope of data that the attacker can obtain, since
 the attacker can access historical data -- the attacker does not have
 to be listening at the time the communication happens.
 Exfiltration attacks can be accomplished via attacks against one of
 the parties to a communication, i.e., by the attacker stealing the
 keys or content rather than the party providing them willingly.  In
 these cases, the party may not be aware, at least at a human level,
 that they are collaborating.  Rather, the subverted technical assets
 are "collaborating" with the attacker (by providing keys/content)
 without their owner's knowledge or consent.
 Any party that has access to encryption keys or unencrypted data can
 be a collaborator.  While collaborators are typically the endpoints
 of a communication (with encryption securing the links),
 intermediaries in an unencrypted communication can also facilitate
 content exfiltration attacks as collaborators by providing the
 attacker access to those communications.  For example, documents
 describing the NSA PRISM program claim that NSA is able to access
 user data directly from servers, where it is stored unencrypted.  In
 these cases, the operator of the server would be a collaborator, if
 an unwitting one.  By contrast, in the NSA MUSCULAR program, a set of
 collaborators enabled attackers to access the cables connecting data
 centers used by service providers such as Google and Yahoo.  Because
 communications among these data centers were not encrypted, the
 collaboration by an intermediate entity allowed the NSA to collect
 unencrypted user data.

Barnes, et al. Informational [Page 16] RFC 7624 Confidentiality Threat Model August 2015

5.2. Attacker Costs

   +--------------------------+-----------------------------------+
   | Attack Class             | Cost / Risk to Attacker           |
   +--------------------------+-----------------------------------+
   | Passive observation      | Passive data access               |
   |                          |                                   |
   | Passive inference        | Passive data access + processing  |
   |                          |                                   |
   | Active                   | Active data access + processing   |
   |                          |                                   |
   | Static key exfiltration  | One-time interaction              |
   |                          |                                   |
   | Dynamic key exfiltration | Ongoing interaction / code change |
   |                          |                                   |
   | Content exfiltration     | Ongoing, bulk interaction         |
   +--------------------------+-----------------------------------+
 Each of the attack types discussed in the previous section entails
 certain costs and risks.  These costs differ by attack and can be
 helpful in guiding response to pervasive attack.
 Depending on the attack, the attacker may be exposed to several types
 of risk, ranging from simply losing access to arrest or prosecution.
 In order for any of these negative consequences to occur, however,
 the attacker must first be discovered and identified.  So, the
 primary risk we focus on here is the risk of discovery and
 attribution.
 A passive pervasive attack is the simplest to mount in some ways.
 The base requirement is that the attacker obtain physical access to a
 communications medium and extract communications from it.  For
 example, the attacker might tap a fiber-optic cable, acquire a mirror
 port on a switch, or listen to a wireless signal.  The need for these
 taps to have physical access or proximity to a link exposes the
 attacker to the risk that the taps will be discovered.  For example,
 a fiber tap or mirror port might be discovered by network operators
 noticing increased attenuation in the fiber or a change in switch
 configuration.  Of course, passive pervasive attacks may be
 accomplished with the cooperation of the network operator, in which
 case there is a risk that the attacker's interactions with the
 network operator will be exposed.
 In many ways, the costs and risks for an active pervasive attack are
 similar to those for a passive pervasive attack, with a few
 additions.  An active attacker requires more robust network access
 than a passive attacker, since, for example, they will often need to
 transmit data as well as receive it.  In the wireless example above,

Barnes, et al. Informational [Page 17] RFC 7624 Confidentiality Threat Model August 2015

 the attacker would need to act as a transmitter as well as a
 receiver, greatly increasing the probability the attacker will be
 discovered (e.g., using direction-finding technology).  Active
 attacks are also much more observable at higher layers of the
 network.  For example, an active attacker that attempts to use a mis-
 issued certificate could be detected via Certificate Transparency
 [RFC6962].
 In terms of raw implementation complexity, passive pervasive attacks
 require only enough processing to extract information from the
 network and store it.  Active pervasive attacks, by contrast, often
 depend on winning race conditions to inject packets into active
 connections.  So, active pervasive attacks in the core of the network
 require processing hardware that can operate at line speed (roughly
 100 Gbps to 1 Tbps in the core) to identify opportunities for attack
 and insert attack traffic in high-volume traffic.  Key exfiltration
 attacks rely on passive pervasive attack for access to encrypted
 data, with the collaborator providing keys to decrypt the data.  So,
 the attacker undertakes the cost and risk of a passive pervasive
 attack, as well as additional risk of discovery via the interactions
 that the attacker has with the collaborator.
 Some active attacks are more expensive than others.  For example,
 active man-in-the-middle (MITM) attacks require access to one or more
 points on a communication's network path that allow visibility of the
 entire session and the ability to modify or drop legitimate packets
 in favor of the attacker's packets.  A similar but weaker form of
 attack, called an active man-on-the-side (MOTS), requires access to
 only part of the session.  In an active MOTS attack, the attacker
 need only be able to inject or modify traffic on the network element
 the attacker has access to.  While this may not allow for full
 control of a communication session (as in an MITM attack), the
 attacker can perform a number of powerful attacks, including but not
 limited to: injecting packets that could terminate the session (e.g.,
 TCP RST packets), sending a fake DNS reply to redirect ensuing TCP
 connections to an address of the attacker's choice (i.e., winning a
 "DNS response race"), and mounting an HTTP redirect attack by
 observing a TCP/HTTP connection to a target address and injecting a
 TCP data packet containing an HTTP redirect.  For example, the system
 dubbed by researchers as China's "Great Cannon" [great-cannon] can
 operate in full MITM mode to accomplish very complex attacks that can
 modify content in transit, while the well-known Great Firewall of
 China is a MOTS system that focuses on blocking access to certain
 kinds of traffic and destinations via TCP RST packet injection.
 In this sense, static exfiltration has a lower risk profile than
 dynamic.  In the static case, the attacker need only interact with
 the collaborator a small number of times, possibly only once -- say,

Barnes, et al. Informational [Page 18] RFC 7624 Confidentiality Threat Model August 2015

 to exchange a private key.  In the dynamic case, the attacker must
 have continuing interactions with the collaborator.  As noted above,
 these interactions may be real, such as in-person meetings, or
 virtual, such as software modifications that render keys available to
 the attacker.  Both of these types of interactions introduce a risk
 that they will be discovered, e.g., by employees of the collaborator
 organization noticing suspicious meetings or suspicious code changes.
 Content exfiltration has a similar risk profile to dynamic key
 exfiltration.  In a content exfiltration attack, the attacker saves
 the cost and risk of conducting a passive pervasive attack.  The risk
 of discovery through interactions with the collaborator, however, is
 still present, and may be higher.  The content of a communication is
 obviously larger than the key used to encrypt it, often by several
 orders of magnitude.  So, in the content exfiltration case, the
 interactions between the collaborator and the attacker need to be
 much higher bandwidth than in the key exfiltration cases, with a
 corresponding increase in the risk that this high-bandwidth channel
 will be discovered.
 It should also be noted that in these latter three exfiltration
 cases, the collaborator also undertakes a risk that his collaboration
 with the attacker will be discovered.  Thus, the attacker may have to
 incur additional cost in order to convince the collaborator to
 participate in the attack.  Likewise, the scope of these attacks is
 limited to cases where the attacker can convince a collaborator to
 participate.  If the attacker is a national government, for example,
 it may be able to compel participation within its borders, but have a
 much more difficult time recruiting foreign collaborators.
 As noted above, the collaborator in an exfiltration attack can be
 unwitting; the attacker can steal keys or data to enable the attack.
 In some ways, the risks of this approach are similar to the case of
 an active collaborator.  In the static case, the attacker needs to
 steal information from the collaborator once; in the dynamic case,
 the attacker needs continued presence inside the collaborators'
 systems.  The main difference is that the risk in this case is of
 automated discovery (e.g., by intrusion detection systems) rather
 than discovery by humans.

6. Security Considerations

 This document describes a threat model for pervasive surveillance
 attacks.  Mitigations are to be given in a future document.

Barnes, et al. Informational [Page 19] RFC 7624 Confidentiality Threat Model August 2015

7. References

7.1. Normative References

 [RFC6973]  Cooper, A., Tschofenig, H., Aboba, B., Peterson, J.,
            Morris, J., Hansen, M., and R. Smith, "Privacy
            Considerations for Internet Protocols", RFC 6973,
            DOI 10.17487/RFC6973, July 2013,
            <http://www.rfc-editor.org/info/rfc6973>.

7.2. Informative References

 [dec1]     Perlroth, N., Larson, J., and S. Shane, "N.S.A. Able to
            Foil Basic Safeguards of Privacy on Web", The New York
            Times, September 2013,
            <http://www.nytimes.com/2013/09/06/us/
            nsa-foils-much-internet-encryption.html>.
 [dec2]     The Guardian, "Project Bullrun -- classification guide to
            the NSA's decryption program", September 2013,
            <http://www.theguardian.com/world/interactive/2013/sep/05/
            nsa-project-bullrun-classification-guide>.
 [dec3]     Ball, J., Borger, J., and G. Greenwald, "Revealed: how US
            and UK spy agencies defeat internet privacy and security",
            The Guardian, September 2013,
            <http://www.theguardian.com/world/2013/sep/05/
            nsa-gchq-encryption-codes-security>.
 [dir1]     Greenwald, G., "NSA collecting phone records of millions
            of Verizon customers daily", The Guardian, June 2013,
            <http://www.theguardian.com/world/2013/jun/06/
            nsa-phone-records-verizon-court-order>.
 [dir2]     Greenwald, G. and E. MacAskill, "NSA Prism program taps in
            to user data of Apple, Google and others", The Guardian,
            June 2013, <http://www.theguardian.com/world/2013/jun/06/
            us-tech-giants-nsa-data>.
 [dir3]     The Guardian, "Sigint -- how the NSA collaborates with
            technology companies", September 2013,
            <http://www.theguardian.com/world/interactive/2013/sep/05/
            sigint-nsa-collaborates-technology-companies>.
 [DPRIVE]   Bortzmeyer, S., "DNS privacy considerations", Work in
            Progress, draft-ietf-dprive-problem-statement-06, June
            2015.

Barnes, et al. Informational [Page 20] RFC 7624 Confidentiality Threat Model August 2015

 [great-cannon]
            Marczak, B., Weaver, N., Dalek, J., Ensafi, R., Fifield,
            D., McKune, S., Rey, A., Scott-Railton, J., Deibert, R.,
            and V. Paxson, "China's Great Cannon", The Citizen Lab,
            University of Toronto, 2015,
            <https://citizenlab.org/2015/04/chinas-great-cannon/>.
 [pass1]    Greenwald, G. and S. Ackerman, "How the NSA is still
            harvesting your online data", The Guardian, June 2013,
            <http://www.theguardian.com/world/2013/jun/27/
            nsa-online-metadata-collection>.
 [pass2]    Ball, J., "NSA's Prism surveillance program: how it works
            and what it can do", The Guardian, June 2013,
            <http://www.theguardian.com/world/2013/jun/08/
            nsa-prism-server-collection-facebook-google>.
 [pass3]    Greenwald, G., "XKeyscore: NSA tool collects 'nearly
            everything a user does on the internet'", The Guardian,
            July 2013, <http://www.theguardian.com/world/2013/jul/31/
            nsa-top-secret-program-online-data>.
 [pass4]    MacAskill, E., Borger, J., Hopkins, N., Davies, N., and J.
            Ball, "How does GCHQ's internet surveillance work?", The
            Guardian, June 2013,
            <http://www.theguardian.com/uk/2013/jun/21/
            how-does-gchq-internet-surveillance-work>.
 [RFC1035]  Mockapetris, P., "Domain names - implementation and
            specification", STD 13, RFC 1035, DOI 10.17487/RFC1035,
            November 1987, <http://www.rfc-editor.org/info/rfc1035>.
 [RFC1918]  Rekhter, Y., Moskowitz, B., Karrenberg, D., de Groot, G.,
            and E. Lear, "Address Allocation for Private Internets",
            BCP 5, RFC 1918, DOI 10.17487/RFC1918, February 1996,
            <http://www.rfc-editor.org/info/rfc1918>.
 [RFC1939]  Myers, J. and M. Rose, "Post Office Protocol - Version 3",
            STD 53, RFC 1939, DOI 10.17487/RFC1939, May 1996,
            <http://www.rfc-editor.org/info/rfc1939>.
 [RFC3261]  Rosenberg, J., Schulzrinne, H., Camarillo, G., Johnston,
            A., Peterson, J., Sparks, R., Handley, M., and E.
            Schooler, "SIP: Session Initiation Protocol", RFC 3261,
            DOI 10.17487/RFC3261, June 2002,
            <http://www.rfc-editor.org/info/rfc3261>.

Barnes, et al. Informational [Page 21] RFC 7624 Confidentiality Threat Model August 2015

 [RFC3365]  Schiller, J., "Strong Security Requirements for Internet
            Engineering Task Force Standard Protocols", BCP 61,
            RFC 3365, DOI 10.17487/RFC3365, August 2002,
            <http://www.rfc-editor.org/info/rfc3365>.
 [RFC3501]  Crispin, M., "INTERNET MESSAGE ACCESS PROTOCOL - VERSION
            4rev1", RFC 3501, DOI 10.17487/RFC3501, March 2003,
            <http://www.rfc-editor.org/info/rfc3501>.
 [RFC4033]  Arends, R., Austein, R., Larson, M., Massey, D., and S.
            Rose, "DNS Security Introduction and Requirements",
            RFC 4033, DOI 10.17487/RFC4033, March 2005,
            <http://www.rfc-editor.org/info/rfc4033>.
 [RFC4303]  Kent, S., "IP Encapsulating Security Payload (ESP)",
            RFC 4303, DOI 10.17487/RFC4303, December 2005,
            <http://www.rfc-editor.org/info/rfc4303>.
 [RFC4949]  Shirey, R., "Internet Security Glossary, Version 2",
            FYI 36, RFC 4949, DOI 10.17487/RFC4949, August 2007,
            <http://www.rfc-editor.org/info/rfc4949>.
 [RFC5246]  Dierks, T. and E. Rescorla, "The Transport Layer Security
            (TLS) Protocol Version 1.2", RFC 5246,
            DOI 10.17487/RFC5246, August 2008,
            <http://www.rfc-editor.org/info/rfc5246>.
 [RFC5321]  Klensin, J., "Simple Mail Transfer Protocol", RFC 5321,
            DOI 10.17487/RFC5321, October 2008,
            <http://www.rfc-editor.org/info/rfc5321>.
 [RFC6962]  Laurie, B., Langley, A., and E. Kasper, "Certificate
            Transparency", RFC 6962, DOI 10.17487/RFC6962, June 2013,
            <http://www.rfc-editor.org/info/rfc6962>.
 [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
            "Specification of the IP Flow Information Export (IPFIX)
            Protocol for the Exchange of Flow Information", STD 77,
            RFC 7011, DOI 10.17487/RFC7011, September 2013,
            <http://www.rfc-editor.org/info/rfc7011>.
 [RFC7258]  Farrell, S. and H. Tschofenig, "Pervasive Monitoring Is an
            Attack", BCP 188, RFC 7258, DOI 10.17487/RFC7258, May
            2014, <http://www.rfc-editor.org/info/rfc7258>.

Barnes, et al. Informational [Page 22] RFC 7624 Confidentiality Threat Model August 2015

 [spiegel1] Appelbaum, J., Horchert, J., Reissmann, O., Rosenbach, M.,
            Schindler, J., and C. Stocker, "NSA's Secret Toolbox: Unit
            Offers Spy Gadgets for Every Need", Spiegel Online,
            December 2013, <http://www.spiegel.de/international/world/
            nsa-secret-toolbox-ant-unit-offers-spy-gadgets-for-every-
            need-a-941006.html>.
 [spiegel2] Appelbaum, J., Gibson, A., Guarnieri, C., Muller-Maguhn,
            A., Poitras, L., Rosenbach, M., Schmundt, H., and M.
            Sontheimer, "The Digital Arms Race: NSA Preps America for
            Future Battle", Spiegel Online, January 2015,
            <http://www.spiegel.de/international/world/new-snowden-
            docs-indicate-scope-of-nsa-preparations-for-cyber-battle-
            a-1013409.html>.
 [TOR1]     Schneier, B., "How the NSA Attacks Tor/Firefox Users With
            QUANTUM and FOXACID", Schneier on Security, October 2013,
            <https://www.schneier.com/blog/archives/2013/10/
            how_the_nsa_att.html>.
 [TOR2]     The Guardian, "'Tor Stinks' presentation -- read the full
            document", October 2013,
            <http://www.theguardian.com/world/interactive/2013/oct/04/
            tor-stinks-nsa-presentation-document>.

IAB Members at the Time of Approval

 Jari Arkko (IETF Chair)
 Mary Barnes
 Marc Blanchet
 Ralph Droms
 Ted Hardie
 Joe Hildebrand
 Russ Housley
 Erik Nordmark
 Robert Sparks
 Andrew Sullivan
 Dave Thaler
 Brian Trammell
 Suzanne Woolf

Barnes, et al. Informational [Page 23] RFC 7624 Confidentiality Threat Model August 2015

Acknowledgements

 Thanks to Dave Thaler for the list of attacks and taxonomy; to
 Security Area Directors Stephen Farrell, Sean Turner, and Kathleen
 Moriarty for starting and managing the IETF's discussion on pervasive
 attack; and to Stephan Neuhaus, Mark Townsley, Chris Inacio,
 Evangelos Halepilidis, Bjoern Hoehrmann, Aziz Mohaisen, Russ Housley,
 Joe Hall, Andrew Sullivan, the IEEE 802 Privacy Executive Committee
 SG, and the IAB Privacy and Security Program for their input.

Authors' Addresses

 Richard Barnes
 Email: rlb@ipv.sx
 Bruce Schneier
 Email: schneier@schneier.com
 Cullen Jennings
 Email: fluffy@cisco.com
 Ted Hardie
 Email: ted.ietf@gmail.com
 Brian Trammell
 Email: ietf@trammell.ch
 Christian Huitema
 Email: huitema@huitema.net
 Daniel Borkmann
 Email: dborkman@iogearbox.net

Barnes, et al. Informational [Page 24]

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