Privacy is an essential part of the Web. This document provides definitions for privacy and related concepts that are applicable worldwide as well as a set of privacy principles that should guide the development of the Web as a trustworthy platform. People using the Web would benefit from a stronger relationship between technology and policy, and this document is written to work with both.
This section describes the status of this document at the time of its publication. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at https://www.w3.org/TR/.
This document is a Draft Finding of the Technical Architecture Group (TAG) which we are releasing as a Draft Note. The intent is for this document to become a W3C Statement. It was prepared by the Web Privacy Principles Task Force, which was convened by the TAG. Publication as a Draft Finding or Draft Note does not imply endorsement by the TAG or by the W3C Membership.
This draft does not yet reflect the consensus of the TAG or the task force and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to cite this document as anything other than a work in progress.
It will continue to evolve and the task force will issue updates as often as needed. At the conclusion of the task force, the TAG intends to adopt this document as a Finding.
This document was published by the Technical Architecture Group as a Group Draft Note using the Note track.
Group Draft Notes are not endorsed by W3C nor its Members.
This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.
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This document is governed by the 12 June 2023 W3C Process Document.
This document elaborates on the privacy principle from the W3C TAG Ethical Web Principles: "Security and privacy are essential." While it focuses on privacy, this should not be taken as an indication that privacy is always more important than other ethical web principles, and this document doesn't address how to balance the different ethical web principles if they come into conflict.
Privacy on the Web is primarily regulated by two forces: the architectural capabilities that the Web platform exposes (or does not), and laws in the various jurisdictions where the Web is used ([New-Chicago-School]). These regulatory mechanisms are separate; a law in one country does not (and should not) change the architecture of the whole Web, and likewise Web specifications cannot override any given law (although they can affect how easy it is to create and enforce law). The Web is not merely an implementation of a particular legal privacy regime; it has distinct features and guarantees driven by shared values that often exceed legal requirements for privacy.
However, the overall goal of privacy on the Web is served best when technology and law complement each other. This document seeks to establish shared concepts as an aid to technical efforts to regulate privacy on the web. It may also be useful in pursuing alignment with and between legal regulatory regimes.
Our goal for this document is not to cover all possible privacy issues, but rather to provide enough background to support the Web community in making informed decisions about privacy and in weaving privacy into the architecture of the Web.
Few architectural principles are absolute, and privacy is no exception: privacy can come into tension with other desirable properties of an ethical architecture, and when that happens the Web community will have to work together to strike the right balance.
The primary audiences for this document are
Additional audiences include:
This document is intended to help its audiences address privacy concerns as early as possible in the life cycle of a new Web standard or feature, or in the development of Web products. Beginning with privacy in mind will help avoid the need to add special cases later to address unforeseen but predictable issues or to build systems that turn out to be unacceptable to users.
Because this document guides privacy reviews of new standards, authors of web specifications should consult it early in the design to make sure their feature passes the review smoothly.
This is a document containing technical guidelines. However, in order to put those guidelines in context we must first define some terms and explain what we mean by privacy.
The Web is a social and technical system made up of information flows. Because this document is specifically about privacy as it applies to the Web, it focuses on privacy with respect to information flows.
The Web is for everyone ([For-Everyone]). It should be "a platform that helps people and provides a net positive social benefit" ([ETHICAL-WEB]). One of the ways in which the Web serves people is by seeking to protect them from surveillance and the types of manipulation that data can enable.
Information can be used to predict and to influence people, as well as to design online spaces that control people's behaviour. The collection and processing of information in greater volume, with greater precision and reliability, with increasing interoperability across a growing variety of data types, and at intensifying speed is leading to a concentration of power that threatens private and public liberties. What's more, automation and the increasing computerisation of all aspects of our lives both increase the power of information and decrease the cost of a number of intrusive behaviours that would be more easily kept in check if the perpetrator had to be in the same room as the victim.
When an actor can collect data about a person and process it automatically, and that person cannot automatically protect their data and prevent its processing (automation asymmetry) this creates an imbalance of power that favors that actor and decreases the person's agency. This document focuses on the impact that data processing can have on people, but it can also impact other actors, such as companies or governments.
It is important to keep in mind that not all people are equal in how they can resist an imbalance of power: some people are more vulnerable and therefore in greater need of protection.
Data governance is the system of principles that regulate information flows. Data governance determines how which actors can collect what data and how they may, must, or must not process it ([GKC-Privacy], [IAD]). This document provides building blocks for data governance that puts people first.
Principles vary from context to context ([Understanding-Privacy], [Contextual-Integrity]). For instance, people have different expectations of privacy at work, at a café, or at home. Understanding and evaluating a privacy situation is best done by clearly identifying:
There are always privacy principles at work. Some sets of principles may be more permissive, but that does not make them neutral. All privacy principles have an impact on people and we must therefore determine which principles best align with ethical Web values in Web contexts ([ETHICAL-WEB], [Why-Privacy]).
Information flows are information exchanged or processed by actors. A person's privacy can be harmed both by their information flowing from them to other actors and by information flowing toward them. Examples of the latter include: unexpected shocking images, loud noises while they intend to sleep, manipulative information, interruptive messages when their focus is on something else, or harassment when they seek social interactions. (In some of these cases, the information may not be personal data.)
On the Web, information flows may involve a wide variety of actors that are not always recognizable or obvious to a user within a particular interaction. Visiting a website may involve the actors that contribute to operating that site, but also actors with network access, which may include: Internet service providers; other network operators; local institutions providing a network connection including schools, libraries or universities; government intelligence services; malicious hackers who have gained access to the network or the systems of any of the other actors. High-level threats including surveillance may be pursued by these actors. Pervasive monitoring, a form of large-scale, indiscriminate surveillance, is a known attack on the privacy of users of the Internet and the Web [RFC7258].
Information flows may also involve other people — for example, other users of a site — which could include friends, family members, teachers, strangers, or government officials. Some threats to privacy, including both disclosure and harassment, may be particular to the other people involved in the information flow.
A person's autonomy is their ability to make decisions of their own personal will, without undue influence from other actors. People have limited intellectual resources and time with which to weigh decisions, and they have to rely on shortcuts when making decisions. This makes it possible to manipulate their preferences, including their privacy preferences ([Privacy-Behavior], [Digital-Market-Manipulation]). A person's autonomy is improved by a system when that system offers a shortcut that is closer to what that person would have decided given unlimited time and intellectual ability. Autonomy is decreased when a similar shortcut goes against decisions made under these ideal conditions.
Affordances and interactions that decrease autonomy are known as deceptive patterns (or dark patterns). A deceptive pattern does not have to be intentional ([Dark-Patterns], [Dark-Pattern-Dark]). When building something that may impact people's autonomy, it is important that reviewers from multiple independent perspectives check that it does not introduce deceptive patterns.
Given the large volume of potential data-related decisions in today's data economy, it is impossible for people to have detailed control over how their data is processed. This fact does not imply that privacy is dead. Studies show that people remain concerned over how their data is processed, that they feel powerless, and sense that they have lost agency ([Privacy-Concerned]). If we design our technological infrastructure carefully, we can give people greater autonomy with respect to their own data. This is done by setting appropriate, privacy-protective defaults and designing user-friendly choice architectures.
Several kinds of mechanisms exist to enable people to control how they interact with data-processing systems. Mechanisms that increase the number of purposes for which their data is being processed or the amount of their data that is processed are referred to as opt-in or consent. Mechanisms that decrease this number of purposes or the amount of data being processed are known as opt-out.
When deployed thoughtfully, these mechanisms can improve people's autonomy. Often, however, they are used as a way to avoid putting in the difficult work of deciding which types of processing are appropriate and which are not, offloading privacy labour to the people using a system.
People should be able to consent to data sharing that would otherwise be restricted, such as granting access to their pictures or geolocation. Actors need to take care that their users are informed when granting this consent and aware enough about what's going on that they can know to revoke their consent when they want to. Consent to data processing and granting permissions to access Web platform APIs are similar problems. Both consent and permissions should be requested in a way that lets people delay or avoid answering if they're trying to do something else. If the user grants some form of persistent access to data, there should be an indicator that lets people notice this ongoing access and that lets them turn it off whenever they wish to. In general, providing consent should be rare, intentional, and temporary.
When an opt-out mechanism exists, it should preferably work with a global opt-out mechanism. The function of a global opt-out mechanism is to rectify the automation asymmetry whereby service providers can automate data processing but people have to take manual action to prevent it. A good example of a global opt-out mechanism is the Global Privacy Control [GPC].
Conceptually, a global opt-out mechanism is an automaton operating as part of the user agent. It is equivalent to a robot that would carry out a person's instructions by pressing an opt-out button (or a similar expression of the person's rights) with every interaction that the person has with a site. (For instance, the person may be objecting to processing based on legitimate interest, withdrawing consent to specific purposes, or requesting that their data not be sold or shared.) The user is effectively delegating the expression of their opt-out to their user agent, which helps rectify automation asymmetry.
Under this model, a global opt-out signal should not be understood as a decision that a person made a while ago when they flipped a setting or chose to use a specific user agent but rather as a preference that they have chosen to automatically reaffirm with every interaction with the site.
One implementation strategy for opt-outs or other data rights is to assign people stable identifiers and to maintain a central registry to map these identifiers to people's preferences. Actors that wish to process a given person's data are then expected to fetch that person's preferences from the central registry and to configure their processing accordingly. This approach has notably been deployed to capture opt-outs of marketing uses of people's phone numbers or residential addresses. This approach is not recommended, for multiple reasons: it offers no technical protection against bad actors, it creates one central point of failure, it is hard to meaningfully audit (particularly for the scale of processing implied by web systems), and experience with existing systems shows that they make it hard for people to exercise their rights.
Privacy labour is the practice of having a person do the work of ensuring data processing of which they are the subject or recipient is appropriate, instead of putting the responsibility on the actors who are doing the processing. Data systems that are based on asking people for their consent tend to increase privacy labour.
More generally, implementations of privacy often offload labour to people. This is notably true of the regimes descended from the Fair Information Practices (FIPs), a loose set of principles initially elaborated in the 1970s in support of individual autonomy in the face of growing concerns with databases. The FIPs generally assume that there is sufficiently little data processing taking place that any person will be able to carry out sufficient diligence to be autonomous in their decision-making. Since they offload the privacy labour to people and assume perfect, unlimited autonomy, the FIPs do not forbid specific types of data processing but only place them under different procedural requirements. This approach is no longer appropriate.
One notable issue with procedural approaches to privacy is that they tend to have the same requirements in situations where people find themselves in a significant asymmetry of power with another actor — for instance a person using an essential service provided by a monopolistic platform — and those where a person and the other actor are very much on equal footing, or even where the person may have greater power, as is the case with small businesses operating in a competitive environment. They also do not consider cases in which one actor may coerce other actors into facilitating its inappropriate practices, as is often the case with dominant players in advertising or in content aggregation ([Consent-Lackeys], [CAT]).
Reference to the FIPs survives to this day. They are often referenced as "transparency and choice", which, in today's digital environment, is often an indication that inappropriate processing is being described.
Privacy principles are defined through social processes and, because of that, the applicable definition of privacy in a given context can be contested ([Privacy-Contested]). This makes privacy a problem of collective action ([GKC-Privacy]). Group-level data processing may impact populations or individuals, including in ways that people could not control even under the optimistic assumptions of consent. For instance, it's possible that the only thing that a person is willing to reveal to a particular actor is that they are part of a given group. However, other members of the same group may be interacting with the same actor and revealing a lot more information, which can enable effective statistical inferences about people who refrain from providing information about themselves.
What we consider is therefore not just the relation between the people who share data and the actors that invite that sharing ([Relational-Turn]), but also between the people who may find themselves categorised indirectly as part of a group even without sharing data. One key understanding here is that such relations may persist even when data is de-identified. What's more, such categorisation of people, voluntary or not, changes the way in which the world operates. This can produce self-reinforcing loops that can damage both individuals and groups ([Seeing-Like-A-State]).
In general, collective issues in data require collective solutions. Web standards help with data governance by defining structural controls in user agents, ensuring that researchers and regulators can discover group-level abuse, and establishing or delegating to institutions that can handle issues of privacy. Governance will often struggle to achieve its goals if it works primarily by increasing individual control instead of by collective action.
Collecting data at large scales can have significant pro-social outcomes. Problems tend to emerge when actors process data for collective benefit and for disloyal purposes at the same time. The disloyal purposes are often justified as bankrolling the pro-social outcomes but this requires collective oversight to be appropriate.
There are different ways for people to become members of a group. Either they can join it deliberately, making it a self-constituted group such as when joining a club, or they can be classified into it by an external actor, typically a bureaucracy or its computerised equivalent ([Beyond-Individual]). In the latter case, people may not be aware that they are being grouped together, and the definition of the group may not be intelligible (for instance if it is created from opaque machine learning techniques).
Protecting group privacy can take place at two different levels. The existence of a group or at least its activities may need to be protected even in cases in which its members are guaranteed to remain anonymous. We refer to this as "group privacy." Conversely, people may wish to protect knowledge that they are members of the group even though the existence of the group and its actions may be well known (e.g. membership in a dissidents movement under authoritarian rule), which we call "membership privacy". An example privacy violation for the former case is the fitness app Strava that did not reveal individual behaviour or legal identity but published heat maps of popular running routes. In doing so, it revealed secret US bases around which military personnel took frequent runs ([Strava-Debacle], [Strava-Reveal-Military]).
People's privacy interests may also be affected when information about a small group of people is processed, even if no individualized data is exposed. For example, browsing activity of the students in a classroom may be sensitive even if their teacher doesn't learn exactly which student accessed a particular resource about a health issue. Targeting presentation of information to a small group may also be inappropriate: for example, targeting messages to people who visited a particular clinic or are empaneled on a particular jury may be invasive even without uniquely individual data.
When people do not know that they are members of a group, when they cannot easily find other members of the group so as to advocate for their rights together, or when they cannot easily understand why they are being categorised into a given group, their ability to protect themselves through self-governing approaches to privacy is largely eliminated.
One common problem in group privacy is when the actions of one member of a group reveal information that other members would prefer were not shared in this way (or at all). For instance, one person may publish a picture of an event in which they are featured alongside others while the other people captured in the same picture would prefer their participation not to be disclosed. Another example of such issues are sites that enable people to upload their contacts: the person performing the upload might be more open to disclosing their social networks than the people they are connected to are. Such issues do not necessarily admit simple, straightforward solutions but they need to be carefully considered by people building websites.
While transparency rarely helps enough to inform the individual choices that people may make, it plays a critical role in letting researchers and reporters inform our collective decision-making about privacy principles. This consideration extends the TAG's resolution on a Strong and Secure Web Platform to ensure that "broad testing and audit continues to be possible" where information flows and automated decisions are involved.
Such transparency can only function if there are strong rights of access to data (including data derived from one's personal data) as well as mechanisms to explain the outcomes of automated decisions.
A user agent acts as an intermediary between a person (its user) and the web. User agents implement, to the extent possible, the principles that collective governance establishes in favour of individuals. They seek to prevent the creation of asymmetries of information, and serve their user by providing them with automation to rectify automation asymmetries. Where possible, they protect their user from receiving intrusive messages.
The user agent is expected to align fully with the person using it and to operate exclusively in that person's interest. It is not the first party. The user agent serves the person as a trustworthy agent: it always puts that person's interest first. In some occasions, this can mean protecting that person from themselves by preventing them from carrying out a dangerous decision, or by slowing down the person in their decision. For example, the user agent will make it difficult for someone to connect to a site if it can't verify that the site is authentic. It will check that that person really intends to expose a sensitive device to a page. It will prevent that person from consenting to the permanent monitoring of their behaviour. Its user agent duties include ([Taking-Trust-Seriously]):
These duties ensure the user agent will care for its user. In academic research, this relationship with a trustworthy agent is often described as "fiduciary" ([Fiduciary-Law], [Fiduciary-Model], [Taking-Trust-Seriously]; see [Fiduciary-UA] for a longer informal discussion). Some jurisdictions may have a distinct legal meaning for "fiduciary." ([Fiduciary-Law])
Many of the principles described in the rest of this document extend the user agent's duties and make them more precise.
While privacy principles are designed to work together and support each other, occasionally a proposal to improve how a system follows one privacy principle may reduce how well it follows another principle.
Given any initial design that doesn't perfectly satisfy all principles, there are usually some other designs that improve the situation for some principles without sacrificing anything about the other principles. Work to find those designs.
Another way to say this is to look for Pareto improvements before starting to trade off between principles.
Once one is choosing between different designs at the Pareto frontier, the choice of which privacy principles to prefer is complex and depends heavily on the details of each particular situation. Note that people's privacy can also be in tension with non-privacy concerns. As discussed in the W3C TAG Ethical Web Principles, "it is important to consider the context in which a particular technology is being applied, the expected audience(s) for the technology, who the technology benefits and who it may disadvantage, and any power dynamics involved" ([Ethical-Web]). Despite this complexity, there is a basic ground rule to follow:
This is a special case of the more general principle that data should not be used for more purposes than the data's subjects understood it was being collected for.
Services sometimes use people's data in order to protect those or other people. A service that does this should explain what data it's using for this purpose. It should also say how it might use or share a person's data if it believes that person has violated the service's rules.
It is attractive to say that if someone violates the rules of a service they're using, then they sacrifice a proportionate amount of their privacy protections, but
The following examples illustrate some of the tensions:
This section describes a set of principles designed to apply to the web context in general. Specific contexts on the web may need more constraints or other considerations. In time, we expect to see more specialized privacy principles published, for more specific contexts on the web.
These principles should be enforced by user agents. When this is not possible, additional enforcement mechanisms are needed.
A person's identity is the set of characteristics that define them. Their identity in a context is the set of characteristics they present under particular circumstances.
People can present different identities to different contexts, and can also share a single identity across several different contexts.
People may wish to present an ephemeral or anonymous identity. This is a set of characteristics that is too small or unstable to be useful for following them through time.
A person's identities may often be distinct from whatever legal identity or identities they hold.
In some circumstances, the best way for a user agent to uphold this principle is to prevent recognition (e.g. so that one site can't learn anything about its user's behavior on another site).
In other circumstances, the best way for a user agent to uphold this principle is to support recognition (e.g. to help its user prove to one site that they have a particular identity on another site).
Similarly, a user agent can help its user by preventing or supporting recognition across repeat visits to the same site.
User agents should do their best to distinguish contexts within a site and adjust their partitions to prevent or support recognition across those intra-site contexts according to their users' wishes.
Data minimization limits the risks of data being disclosed or misused. It also helps user agents and other actors more meaningfully explain the decisions their users need to make. For more information, see Data Minimization in Web APIs.
Web APIs should be designed to minimize the amount of data that sites need to request to pursue their users' goals and interests. They should also provide granular user controls over personal data that is communicated to sites.
The principle of data minimization applies to all personal data, even if it is not known to be identifying, sensitive, or otherwise harmful. See: 2.4 Sensitive Information.
In order to uphold the principle of 2.2 Data Minimization, sites and user agents should seek to understand and respect people's goals and preferences about use of data about them.
Sites sometimes use data in ways that aren't needed for the user's immediate goals. These uses are known as ancillary uses, and data that is primarily useful for ancillary uses is ancillary data.
Different users will want to share different kinds and amounts of ancillary data with sites. Some people will not want to share any ancillary data at all.
Users may be willing to share ancillary data if it is aggregated with the data of other users, or de-identified. This can be useful when ancillary data contributes to a collective benefit in a way that reduces privacy threats to individuals (see collective privacy).
User agents should aggressively minimize ancillary data and should avoid burdening the user with additional privacy labor when deciding what ancillary data to expose. To that end, user agents may employ user research, solicitation of general preferences, and heuristics about sensitivity of data or trust in a particular context.
To help sites understand user preferences, user agents can provide browser-configurable signals to directly communicate common user preferences (such as a global opt-out).
Data exposed for the ancillary uses of telemetry and analytics may reveal information about user configuration, device, environment, or behavior that could be used as part of browser fingerprinting to identify users across sites. Revealing user preferences or other heuristics in providing or disabling functionality could also contribute to a browser fingerprint.
Functionality for telemetry and analytics should be explicitly noted by specification authors, to help user agents provide configuration options to their users.
The many APIs available to websites expose lots of data that can be combined into information about people, web servers, and other things.
User-controlled settings or permissions can guard access to data on the web. When designing a Web API, use access guards to ensure the API exposes information in appropriate ways.
New APIs which add new ways of getting information must be guarded at least as strongly as the existing ways.
Information that would be acceptable to expose under one set of access guards might be unacceptable under another set. When an API designer intends to explain that their new API is acceptable because an existing acceptable API already exposes the same information, they must be careful to ensure that their new API is only available under a set of guards that are at least as strict. Without those guards, they need to make the argument from scratch, without relying on the existing API.
If existing APIs provide access to some information, but there is a plan to change those APIs to prevent that access, new APIs must not be added that provide that same information, unless they include additional access guards that ensure access is appropriate.
For example, browsers are gradually removing the ability to join identities between different partitions. It is important that new APIs do not add features which re-enable cross-context recognition.
Many pieces of information about someone could cause privacy harms if disclosed. For example:
A particular piece of information may have different sensitivity for different people. People can become vulnerable if sensitive information about them is, or is likely to be, exposed; see 2.10 Vulnerability.
When considering whether a class of information is likely to be sensitive to a person, consider at least these factors:
While data rights alone are not sufficient to satisfy all privacy principles for the Web, they do support self-determination and help improve accountability. Such rights include:
This right includes both being able to review what information has been collected or inferred about oneself and being able to discover what actors have collected information about oneself. As a result, databases cannot be kept secret and data collected about people needs to be meaningfully discoverable by those people.
The right to erase applies whether or not terminating use of a service altogether, though what data can be erased may differ between those two cases. On the Web, people may wish to erase data on their device, on a server, or both, and the distinctions may not always be clear.
Portability is needed to realize the ability for people to make choices about services with different data practices. Standards for interoperability are essential for effective re-use.
The right to be free from automated decision-making based on data about oneself.
For some kinds of decision-making with substantial consequences, there is a privacy interest in being able to exclude oneself from automated profiling. For example, some services may alter the price of products (price discrimination) or offers for credit or insurance based on data collected about a person. Those alterations may be consequential (financially, say) and objectionable to people who believe those decisions based on data about them are inaccurate or unjust. As another example, some services may draw inferences about a user's identity, humanity, or presence based on facial recognition algorithms run on camera data. Because facial recognition algorithms and training sets are fallible and may exhibit certain biases, people may not wish to submit to decisions based on that kind of automated recognition.
People may change their decisions about consent or may object to subsequent uses of data about themselves. Retaining rights requires ongoing control, not just at the time of collection.
The OECD Privacy Principles [OECD-Guidelines], [Records-Computers-Rights], and the [GDPR], among other places, include many of the rights people have as data subjects. These participatory rights by people over data about themselves are inherent to autonomy.
Data is de-identified when there exists a high level of confidence that no person described by the data can be identified, directly or indirectly (e.g. via association with an identifier, user agent, or device), by that data alone or in combination with other available information. Note that further considerations relating to groups are covered in the Collective Issues in Privacy section.
We talk of controlled de-identified data when:
Different situations involving controlled de-identified data will require different controls. For instance, if the controlled de-identified data is only being processed by one actor, typical controls include making sure that the identifiers used in the data are unique to that dataset, that any person (e.g. an employee of the actor) with access to the data is barred (e.g. based on legal terms) from sharing the data further, and that technical measures exist to prevent re-identification or the joining of different data sets involving this data.
In general, the goal is to ensure that controlled de-identified data is used in a manner that provides a viable degree of oversight and accountability such that technical and procedural means to guarantee the maintenance of pseudonymity are preserved.
This is more difficult when the controlled de-identified data is shared between several actors. In such cases, good examples of typical controls that are representative of best practices would include making sure that:
the identifiers used in the data are under the direct and exclusive control of the first party (the actor a person is directly interacting with) who is prevented by strict controls from matching the identifiers with the data;
when these identifiers are shared with a third party, they are made unique to that third party such that if they are shared with more than one third party these cannot then match them up with one another;
technical measures exist to prevent re-identification or the joining of different data sets involving this data; and
Note that controlled de-identified data, on its own, is not sufficient to make data processing appropriate.
Privacy principles are often defined in terms of extending rights to individuals. However, there are cases in which deciding which principles apply is best done collectively, on behalf of a group.
One such case, which has become increasingly common with widespread profiling, is that of information relating to membership of a group or to a group's behaviour, as detailed in 1.2.1 Group Privacy. As Brent Mittelstadt explains, “Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf.” ([Individual-Group-Privacy]) This justifies ensuring that grouped people can benefit from both individual and collective means to support their autonomy with respect to data processing. It should be noted that processing can be unjust even if individuals remain anonymous, not from the violation of individual autonomy but because it violates ideals of social equality ([Relational-Governance]).
Another case in which collective decision-making is preferable is for processing for which informed individual decision-making is unrealistic (due to the complexity of the processing, the volume or frequency of processing, or both). Expecting laypeople (or even experts) to make informed decisions relating to complex data processing or to make decisions on a very frequent basis — even if the processing is relatively simple — is unrealistic if we also want them to have reasonable levels of autonomy in making these decisions.
The purpose of this principle is to require that data governance provide ways to distinguish appropriate data processing without relying on individual decisions whenever the latter are impossible, which is often ([Relational-Governance], [Relational-Turn]).
Which forms of collective governance are recognised as legitimate will depend on domains. These may take many forms, such as governmental bodies at various administrative levels, standards organisations, worker bargaining units, or civil society fora.
It must be noted that, even though collective decision-making can be better than offloading privacy labour to individuals, it is not necessarily a panacea. When considering such collective arrangements it is important to keep in mind the principles that are likely to support viable and effective institutions at any level of complexity ([IAD]).
A good example of a failure in collective privacy decisions was the standardisation of the
ping attribute. Search engines, social sites, and other algorithmic media in the same vein
have an interest in knowing which sites that they link to people choose to visit (which in turn
could improve the service for everyone). But people may have an interest in keeping that
information private from algorithmic media companies (as do the sites being linked to, as that
facilitates timing attacks to recognise people there). A person's exit through a specific
and difficult for user agents to defend against. The value proposition of the
in this context is therefore straightforward: by providing declarative support for this
functionality it can be made fast (the browser sends an asynchronous notification to a ping
endpoint after the person exits through a link) and the user agent can provide its user with
the option to opt out of such tracking — or disable it by default.
Unfortunately, this arrangement proved to be unworkable on the privacy side (the performance gains,
however, are real). What prevents a site from using
ping for people who have it activated
and bounce tracking for others? What prevents a browsers from opting everyone out because it wishes
to offer better protection by default? Given the contested nature of the
ping attribute and
the absence of a forcing function to support collective enforcement, the scheme failed to deliver
Computing devices have owners, who have administrator access to the devices in order to install and configure the programs that run on them. As a program running on a device, a user agent generally can't tell whether the administrator who has installed and configured it was authorized by the device's actual owner.
Sometimes the person using a device doesn't own the device or have administrator access to it (e.g. an employer providing a device to an employee; a friend loaning a device to their guest; or a parent providing a device to their young child). Other times, the owner and primary user of a device might not be the only person with administrator access.
These relationships can involve power imbalances. A child may have difficulty accessing any computing devices other than the ones their parent provides. A victim of abuse might not be able to prevent their partner from having administrator access to their devices. An employee might have to agree to use their employer's devices in order to keep their job.
While a device owner has an interest and sometimes a responsibility to make sure their device is used in the ways they intended, the person using the device still has a right to privacy while using it. This principle enforces this right to privacy in two ways:
Some administrator requests might be reasonable for some sorts of users, like employees or children, but not be reasonable for other sorts, like friends or intimate partners. The user agent should explain what the administrator is going to learn in a way that helps different users to react appropriately.
Online harassment is the "pervasive or severe targeting of an individual or group online through harmful behavior" [PEN-Harassment]. Harassment is a prevalent problem on the web, particularly via social media. While harassment may affect any person using the web, it may be more severe and its consequences more impactful for LGBTQ people, women, people in racial or ethnic minorities, people with disabilities, vulnerable people and other marginalized groups.
Harassment is both a violation of privacy itself and can be enabled or exacerbated by other violations of privacy.
Harassment may include: sending unwanted information; directing others to contact or bother a person ("dogpiling"); disclosing sensitive information about a person; posting false information about a person; impersonating a person; insults; threats; and hateful or demeaning speech.
Disclosure of identifying or contact information (including "doxxing") can often be used to cause additional attackers to send persistent unwanted information that amounts to harassment. Disclosure of location information can be used to intrude on a person's physical safety or space.
Reporting mechanisms are mitigations, but may not prevent harassment, particularly in cases where hosts, moderators, or other intermediaries are supportive of or complicit in the abuse.
Effective reporting is likely to require:
Unwanted information covers a broad range of unsolicited communication, from messages that are typically harmless individually but that become a nuisance in aggregate (spam) to the sending of explicit, graphic, or violent images.
System designers should take steps to make the sending of unwanted information more difficult or more costly, and to make the senders more accountable.
This section is still being refined. We expect additional principles to be added.
Sometimes particular groups are classed as “vulnerable” (e.g. children, or the elderly), but anyone could become privacy vulnerable in a given context. A person may not realise when they disclose personal data that they are vulnerable or could become vulnerable.
Some individuals may be more vulnerable to privacy risks or harm as a result of collection, misuse, loss or theft of personal data because:
Additional privacy protections may be needed for personal data of vulnerable people or sensitive information which could cause someone to become vulnerable if their personal data is collected, used or shared (e.g. blocking tracking elements, sensor data or information about installed software or connected devices).
While sometimes others can help vulnerable people assess privacy risks and make decisions about privacy (such as parents, guardians and peers), everyone has their own right to privacy.
Some vulnerable people need a guardian to help them make good decisions about their own web use (e.g. children, with their parents often acting as their guardians). A person with a guardian is known as a ward.
The ward has a right to make informed decisions and exercise their autonomy regarding their right to privacy. Their guardian has an obligation to help their ward do so when the ward's abilities aren't sufficient, even if that conflicts with the guardian's desires. In practice, many guardians do not make decisions in their ward's best interest, and it's critical that web platform technologies do not exacerbate the risks inherant in this situation.
User agents should balance a benevolent guardian's need to protect their ward from dangers, against a wards' need to protect themselves if they have a malicious guardian.
User agents can protect vulnerable wards by complying with the principles in 2.8 Device Owners and Administrators, and may only provide information about a ward to a guardian for the purpose of helping that guardian uphold their responsibilities to their ward. The mechanism for doing so must include measures to help wards who realize that their guardian isn't acting in the ward's interest.
Attempts to obtain consent to processing that is not in accordance with the person's true preferences result in imposing unwanted privacy labour on the person, and may result in people erroneously giving consent that they regret later.
An actor should not prompt a person for consent if the person is unlikely to have sufficient information to make an informed decision to consent or not. In considering whether or not a person is sufficiently informed to be asked for consent, actors should be realistic in assessing how much time and effort would be required to understand the processing for which they are asking for consent. Simply providing a link to a complex policy is unlikely to mean that the person is informed.
Examples of alternatives to interrupting users with consent requests include:
Considering the information sharing norms in the site's audience and category, and requesting only consent that is appropriate to the purpose of the site. (For example, a photo sharing site's users might expect to be prompted for consent to share their uploaded work.) Sites should consider conducting user research on people's expectations for how data is processed.
Delaying a prompt for consent until a user does something that puts the request in context, which will also help them give an informed response.
A person may share data about other people (e.g. a picture with both that person and others). If that person consents to the processing of that data, this does not imply that those other people have consented as well.
Notifications and other interruptive UI can be a powerful way to capture attention. Depending on the operating system in use, a notification can appear outside of the browser context (for example, in a general notifications tray) or even cause a device to buzz or play an alert tone. Like all powerful features, notifications can be misused and can become an annoyance or even used to manipulate behaviour and thus reduce autonomy.
User agents should provide UI that allows their users to audit which web sites have been granted permission to display alerts and to revoke these permissions. User agents should also apply some quality metric to the initial request for permissions to receive notifications (for example, disallowing sites from requesting permission on first visit).
Web sites should tell their users what specific kind of information people can expect to receive, and how notifications can be turned off, when requesting permission to send interruptive notifications. Web sites should not request permission to send notifications when the user is unlikely to have sufficient knowledge (e.g. information about what kinds of notifications they are signing up for) to make an informed response. If it's unlikely that such information could have been provided then the user agent should apply mitigations (for example, warning about potential malicious use of the notifications API). Permissions should be requested in context.
Whenever people have the ability to cause an actor to process less of their data or to stop carrying out some given set of data processing that is not essential to the service, they must be allowed to do so without the actor retaliating, for instance by artificially removing an unrelated feature, by decreasing the quality of the service, or by trying to cajole, badger, or trick the person into opting back into the processing.
Actors can invest time and energy into automating ways of gathering data from people and can design their products in ways that make it a lot easier for people to disclose information than not, whereas people typically have to manually wade through options, repeated prompts, and deceptive patterns. In many cases, the absence of data — when a person refuses to provide some information — can also be identifying or revealing. Additionally, APIs can be defined or implemented in rigid ways that can prevent people from accessing useful functionality. For example, I might want to look for restaurants in a city I will be visiting this weekend, but if my geolocation is forcefully set to match my GPS, a restaurant-finding site might only allow searches in my current location. In other cases, sites do not abide by data minimisation principles and request more information than they require. This principle supports people in minimising their own data.
User agents should make it simple for people to present the identity they wish to and to provide information about themselves or their devices in ways that they control. This helps people to live in obscurity ([Lost-In-Crowd], [Obscurity-By-Design]), including by obfuscating information about themselves ([Obfuscation]).
Instead, the API could indicate a person's preference, a person's chosen identity, a person's query or interest, or a person's selected communication style.
For example, a user agent might support this principle by:
Sites should include deception in their threat modeling and not assume that Web platform APIs provide any guarantees of consistency, currency, or correctness about the user. People often have control of the devices and software they use to interact with web sites. In response to site requests, people may arbitrarily modify or select the information they provide for a variety of reasons, including both malice and self-protection.
In any rare instances when an API must be defined as returning true current values, users may still configure their agents to respond with other information, for reasons including testing, auditing or mitigating forms of data collection, including browser fingerprinting.
A person (also user or data subject) is any natural person. Throughout this document, we primarily use person or people to refer to human beings, as a reminder of their humanity. When we use the term user, it is to talk about the specific person who happens to be using a given system at that time.
A vulnerable person is a person who may be unable to exercise sufficient self-determination in a context. Amongst other things, they should be treated with greater default privacy protections and may be considered unable to consent to various interactions with a system. People can be vulnerable for different reasons, for example because they are children, are employees with respect to their employers, are facing a steep asymmetry of power, are people in some situations of intellectual or psychological impairment, are refugees, etc.
A context is a physical or digital environment in which people interact with other actors, and which the people understand as distinct from other contexts.
A context is not defined in terms of who owns or controls it. Sharing data between different contexts of a single company is a privacy violation, just as if the same data were shared between unrelated actors.
An actor is an entity that a person can reasonably understand as a single "thing" they're interacting with. Actors can be people or collective entities like companies, associations, or governmental bodies. Uses of this document in a particular domain are expected to describe how the core concepts of that domain combine into a user-comprehensible actor, and those refined definitions are likely to differ between domains.
User agents tend to explain to people which origin or site provided the web page they're looking at. The actor that controls this origin or site is known as the web page's first party. When a person interacts with a UI element on a web page, the first party of that interaction is usually the web page's first party. However, if a different actor controls how data collected with the UI element is used, and a reasonable person with a realistic cognitive budget would realize that this other actor has this control, this other actor is the first party for the interaction instead.
The first party to an interaction is accountable for the processing of data produced by that interaction, even if another actor does the processing.
A third party is any actor other than the person visiting the website or the first parties they expect to be interacting with.
The Vegas Rule is a simple implementation of privacy in which "what happens with the first party stays with the first party." Put differently, the Vegas Rule is followed when the first party is the only data controller. While the Vegas Rule is a good guideline, it's neither necessary nor sufficient for appropriate data processing. A first party that maintains exclusive access to a person's data can still process it inappropriately, and there are cases where a third party can learn information about a person but still treat it appropriately.
We define personal data as any information that is directly or indirectly related to an identified or identifiable person, such as by reference to an identifier ([GDPR], [OECD-Guidelines], [Convention-108]).
On the web, an identifier of some type is typically assigned for an identity as seen by a website, which makes it easier for an automated system to store data about that person.
Examples of identifiers for a person can be:
If a person could reasonably be identified or re-identified through the combination of data with other data, then that data is personal data.
Privacy is achieved in a given context that either involves personal data or involves information being presented to people when the principles of that context are followed appropriately. When the principles for that context are not followed, there is a privacy violation. Similarly, we say that a particular interaction is appropriate when the principles are adhered to) or inappropriate otherwise.
An actor processes data if it carries out operations on personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, sharing, dissemination or otherwise making available, selling, alignment or combination, restriction, erasure or destruction.
An actor shares data if it provides it to any other actor. Note that, under this definition, an actor that provides data to its own service providers is not sharing it.
An actor sells data when it shares it in exchange for consideration, monetary or otherwise.
The purpose of a given processing of data is an anticipated, intended, or planned outcome of this processing which is achieved or aimed for within a given context. A purpose, when described, should be specific enough to be actionable by someone familiar with the relevant context (ie. they could independently determine means that reasonably correspond to an implementation of the purpose).
The means are the general method of data processing through which a given purpose is implemented, in a given context, considered at a relatively abstract level and not necessarily all the way down to implementation details. Example: a person will have their preferences restored (purpose) by looking up their identifier in a preferences store (means).
A data controller is an actor that determines the means and purposes of data processing. Any actor that is not a service provider is a data controller.
A service provider or data processor is considered to be in the same category of first party or third party as the actor contracting it to perform the relevant processing if it:
Recognition is the act of realising that a given identity corresponds to the same person as another identity which may have been observed either in another context, or in the same context but at a different time. Recognition can be probabilistic, if someone realises there's a high probability that two identities correspond to the same person, even if they aren't certain.
A person can be recognized whether or not their legal identity or characteristics of their legal identity are included in the recognition.
There are several types of recognition that may take place.
Cross-context recognition is recognition between different contexts.
Cross-context recognition is only appropriate when the person being recognized can reasonably expect recognition to happen, and can control whether it does.
If a person uses a piece of identifying information in two different contexts (e.g. their email or phone number), this does not automatically mean that they intend to use the same identity in both contexts. It is inappropriate to recognize them using that information, unless there's some other indication that they intended to use a single identity. It is also inappropriate to seek extra identifying information to help with cross-context recognition.
Systems which recognize people across contexts need to be careful not to apply the principles of one context in ways that violate the principles around use of information acquired in a different context. This is particularly true for vulnerable people, as recognising them in different contexts may force traits into the open that reveal their vulnerability. For example, if you meet your therapist at a party, you expect them to have different discussion topics with you than they usually would, and possibly even to pretend they don't know you.
Cross-site recognition is recognition when the identities are observed on different sites. In the usual case that the sites are different contexts, cross-site recognition is a privacy harm in the same cases as cross-context recognition.
Same-site recognition is when a single site recognizes a person across two or more visits.
A privacy harm occurs if a person reasonably expects that they'll be using a different identity for different visits to a single site, but the site recognizes them anyway.
Note that these categories overlap: cross-site recognition is usually cross-context recognition (and always recognizes across partitions); and same-site recognition is sometimes cross-context recognition (and may or may not involve multiple partitions).
A partition is the user agent's attempt to match how its user would understand a context. User agents don't have a perfect understanding of how their users experience the sites they visit, so they often need to approximate the boundaries between contexts when building partitions.
In the absence of better information, a partition can be defined as:
iframes, workers, and top-level pages)
It can be difficult for a user agent to detect when a single site contains multiple contexts. When a user agent can detect this, it should adjust its partitions accordingly, for instance by partitioning identities per subdomain or site path. User agents should work to improve their ability to distinguish contexts within a site.
User agents should prevent people from being recognized across partitions unless they intend to be recognized.
Note that sites can do harm even if they can't be completely certain that visits come from the same person, so user agents should also take steps to prevent such probabilistic recognition. The Target Privacy Threat Model discusses the tradeoffs involved ([Privacy-Threat]).
If a user agent can tell that its user is using a particular identity on a website, it should make that active identity clear to the user (e.g. if the user logged into the site via an API like Credential Management Level 1).
User agents should attempt to defend the people using them from a variety of high-level threats or attacker goals, described in this section.
These threats are an extension of the ones discussed by [RFC6973].
These threats combine into the particular concrete threats we want web specifications to defend against, described in the sections that follow.
Some of the definitions in this document build on top of the work in Tracking Preference Expression (DNT).
The following people, in alphabetical order of their first name, were instrumental in producing this document and made invaluable contributions: Amy Guy, Ben Savage, Chris Needham, Christine Runnegar, Dan Appelquist, Don Marti, François Daoust, Ian Jacobs, Irene Knapp, Jonathan Kingston, Kyle Den Hartog, Mark Nottingham, Martin Thomson, Nick Doty, Peter Snyder, Sam Weiler, Shubhie Panicker, Tess O'Connor, and Wendy Seltzer.