---
# System prepended metadata

title: Selective Correlation

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Syncs to -> https://github.com/WebOfTrustInfo/rwot11-the-hague/blob/master/draft-documents/selective-correlation.md

Contact @ChristopherA if you want push a change from HackMD.

Selective Correlation 
==================================
***Data Minimization, Selective Disclosure, and Progressive Trust***

# To Do

* [ ] Enumerate and describe "capabilities" of various technologies, with regard to correlation
  * [ ] ASSIGNED @peacekeeper 
* [ ] Progressive trust
    * [ ] @Brent @ChristopherA 
* [ ] Use Cases
    * [ ] Educational @ChristopherA 
    * [ ] Healthcare @elenachachkarova
* [ ] Definitions
    * [ ] @fabtagliaferro
* [ ] @ChristopherA would like to see if we can explore WHY parties (in particular holders≠subject) are motivated to elide and otherwise protect personal data they hold, other than because regulators or a contract requires it. Such as to avoid legal costs (discovery, subpoenas, etc.), avoid competitive risks (employee poaching, front-loading transactions), etc. With this we might be able address positive incentives.
    * [ ] The is related to the concept of "non-designated holders" of verified credentials that received a VC, verified it, but also keeps it and could send it to yet another party. I currently am recieving spam mail offers for Holland events since clearly my airline has sold that I've arrived here.

# Authors

* Brent Zundel (lead author) - @brentzundel (GitHub), brent.zundel@gendigital.com, [Gen Digital](https://www.gendigital.com/)
* Christopher Allen - @ChristopherA (GitHub & Twitter), ChristopherA@LifeWithAlacrity.com - [Blockchain Commons](https://www.BlockchainCommons.com)
* Markus Sabadello - @peacekeeper (GitHub), [Danube Tech](https://danubetech.com/)
* Elena Chachkarova - @elenachachkarova (GitHub), [Nymlab](https://www.nymlab.it/#/)
* Fabio Tagliaferro - @fabtagliaferro (GitHub), [University of Verona](https://www.di.univr.it/?ent=persona&id=39578&lang=en), [Commercio.Network](https://commercio.network/)

# Introduction

The goal of this paper is to enable decision makers, particularly non-technical ones, to gain a nuanced grasp of correlation. We will explore the concept of selective correlation, introduce a threat model, and propose a framework within which policies such as data minimization can be successfully actualized. We will strive to do so in plain English, but with some rigor. This will enable readers of this paper to better understand correlation best practices, to adopt correct approaches for their use cases, and to assess techniques that enable these best practices.

## Correlation and Identity

Correlation lies at the heart of identity. The concept of identity cannot exist without some obverver who identifies by correlating different data points about the subject being identified. Identity is correlation across contexts: the subject being identified is recognized as the same subject from a different context. Therefore, control of correlation is directly tied to control of identity, or rather, tied to control of how identifiable the subject is.[^1] 

In the context of self-sovereign identity, the power to correlate must also be self-sovereign. That is, the power to correlate must be, as much as possible, in the hands of the individual being correlated, as the relative risks to individuals are greater.[?]

In most systems, individuals have little control about what information they provide. Much of the information that will allow them to be correlated lies outside of their control. In a self-sovereign system, individuals have more opportunities to manage what data will be correlated.

We define correlatable data as any data than can be used to identify a subject across contexts. A context may be siloed, as when data is shared with two different verifiers. Or it may be temporal, as when data is shared with the same verifier over time. 

We define an observer as an entity who collects correlatable data about a subject. An observer may collect data directly from subjects, from the process used to collect data about subjects, or from other observers. The more correlatable data that is available, the easier it will be for an observer to identify subjects without their consent. Therefore, a reduction in the set of available correlatable data is necessary. To accomplish this we recommend in general the practice of data minimization.

We define data minimization as the privacy-preserving practice of only revealing the data necessary and sufficient for parties to transact together to minimize risk of all parties. We cover this in greater detail below.

## A Classification System for Correlatable Data
Using the context of a verifiable credential ecosystem, we classify data according to its relationship to the subject. For this classification we imagine the following system:

1. There are credentials, which contain **claim data** about a subject, e.g., the claims contained in the `credentialSubject` property of a Verifiable Credential[^2]. Claim data may include things like a subject's name, identifier, or date of birth.
2. The credentials are created by an issuer. **Credential data** is data about the credential, e.g., the data in the `issuer` and `id` properties of a verifiable credential. Credential data may include metadata about the claims themselves, e.g., the value of the `evidence` property in a verifiable credential or some of the claims in the `credentialSubject` property. Credential data may be an identifier for the issuer, or the name of the doctor who delivered the child named in the birth certificate credential.
3. The credentials are exchanged by some protocol from the issuer to a holder, and from the holder to a verifier. **Protocol data** is data related to the protocols used to exchange credentials or make a presentation of them, e.g., the `authorization_details` in OIDC4VC[^3] or `created_time` in DIDComm[^4].
4. The protocol operates upon some existing internet insfrastructure. **Infrastructure Data** is data related to this infrastructure, e.g., the IP address of the client device which receives the issued credential.

The classification uses examples from existing components to help illustrate and clarify, but the intent for the classification to be generally applicable.

## Correlation

There are seven types of correlation:
* Desirable Correlation — the association between the party's datasets is necessary for the process, either for identification, authentication, authorization, or provenance.
* Risky Correlation - the association between the parties' datasets could be used against the benefit of the parties.
* Temporary Correlation - the correlation can be repudiated in the future easily.
* Transient Correlation - the correlation can be observed for a limited amount of time and thus is not permanent.
* Transitive Correlation - the observer correlates the subject indirectly, that is, without the subject being involved in the collection of data for example by passing a credential.
* Correlation Using Neither Identifiers Nor Consent -
* Quasi Correlation - correlation that is not certain, usually based on experience of the observer about particular characteristics of the data
* Spontaneous Correlation - 
* Unwanted or Ambient Correlation - the correlation can be observed from the environment where the subject is included, usually observing the (digital) footprint -> example: [Am I Unique - Learn how identifiable you are on the Internet](https://amiunique.org/)

TODO
* can it even be avoided?
* herd privacy: (see below) https://w3c.github.io/did-core/#herd-privacy & https://github.com/w3c/did-core/issues/539
* differential privacy (see below, but can we even do it\) https://privacytools.seas.harvard.edu/differential-privacy

# Data Minimization
Data minimization is the act of limiting the amount of shared data strictly to the minimum necessary in order to successfully accomplish a task or goal while minimizing risk.

[//]: # (@ChristopherA: Data Minimization is a privacy-preserving practice that only reveals what is necessary and sufficient for parties to trust each other and transact together to minimize risk.)

There are three types of minimization:

* Content minimization – the amount of data should be strictly the minimum necessary.
* Temporal minimization – the data should be stored by the receiver strictly for the minimum amount of time necessary to execute the task.
* Scope minimization – the data should only be used for the strict purpose of the active task. 

[//]: # (@ChristopherA: should we explain why for each these? herd privacy? avoid potential collection of "toxic" GDPR data; ??)
[//]: # (@fabtagliaferro: GDPR added to verifier desires for data minimization below, herd privacy explained in short definition below)

Data minimization is enacted primarily by policy decisions made by stakeholders in the credentials ecosystem:
* Credential issuers ensure that credentials may be presented in such a way as to enable data minimization. This may require issuing multiple, related, granular sub-credentials. 
* Credential inspectors establish in advance policies regarding the data they will examine: 
	* what is the minimum data necessary to accomplish the task or goal?
	* what is the minimum time the data can be stored to execute the task?
	* what processes ensure that the data is applied only to the task at hand and does not, by a process of scope creep, become applied to other tasks or goals?

[//]: # (@ChristopherA: I'd love to see reasons that subjects desire data minimization, and even better, when holders/verifiers ≠ subject desire data minimization.)

A subject desires data minimization for various reasons:
* Subjects can wait to share some information and provide them gradually (Progressive Trust)
* Reduce the risk of undesired correlation 

A verifier desires data minimization for various reasons:
* Not appearing threatening (requesting unrelevant information will confuse the user and let him consider refusing to share any information at all)
* Protection from "I told you so" situations
* Avoid potential collection of "toxic" GDPR data
* Reduce the cost of storing information


Data minimization policies impact selective disclosure, the next privacy enhancement.

## Definitions / Terminology

**Correlatable**: Data is said to be correlatable if by examining it that there is a way to determine whether it is associated with sets of other data stored elsewhere. This also includes lossy projections of the sets of data such as deterministic hashes that demonstrate that assocation.

**Decorrelation**: A general term for any process that is used to reduce correlation within data, or cross-correlation with other sets of data, while preserving other useful aspects of the data. 

**Differential Privacy**: A decorrelation process for sharing information about sets of data by describing the patterns of groups within the dataset while withholding information about individuals in the dataset. The idea behind differential privacy is that if the effect of making an arbitrary single substitution in the database is small enough, the query result cannot be used to infer much about any single individual, and therefore provides privacy. For instance, adding a random number (say 4) from one part of the set and subtracting 4 from another part of the set when the business purpose of the data is total or average.

**Elide/Elision**: The term elide means "to leave out", and elision is the act or instance of ommitting something. (Redaction is a related term

**Herd Privacy**: The decorrelation choices made by one set vs other data sets may result in correlation. Herd privacy process ensures that this doesn't happen by making them indistiguishable from other data sets.

**Non-correlatable**: If there is no practical way to learn whether a set of data is a projection of a other data, they are said to be noncorrelatable.

**Nonce**: aka "Number Used Once". This is an arbitrary number that is used just once in a cryptographic function such as signatures or encryption, to prevent correlation. Note that this is not necessarly a random number, simply a number that is never used again, and in some cases it can be quite valuable for a nonce to be generated deterministically.

**Quasi-correlatable**: Between projections that are definitely correlatable and definitely noncorrelatable, there are projections that may leak a little information about their data. In particular (in no particular order), size, order, use of particular formatting, date/time, location, algorithm usage, and other identifiable patterns.

**Salt**: This is random data (values and length) that are used as an additional input to a cryptographic function such as a hash of data, encryption, signature, to prevent correlation.

## Capabilities and Techniques

### Elision
Elision is the ability of an individual to derive from a credential another credential with some attributes removed. Stakeholders in the credentials ecosystem enable elision capabilities in the following ways:
* Credential issuers negotiate with the holder to enable elision, by issuing credentials with a subset of attributes already included in another credential. 
* Credential inspectors ensure that the elided credentials can be verified with the usual procedure, but they won't be able to know if any kind of elision has been performed if they cannot correlate them with the starting credentials. 

### Selective disclosure
Selective disclosure is the ability of an individual to granularly decide what information to share. Stakeholders in the credentials ecosystem enable selective disclosure capabilities in the following ways:
* Credential issuers format the credential and its attributes in such a way as to enable selective disclosure. As with the strategy of data minimization, they may issue multiple, related, granular sub-credentials. Each attribute and the overall credential may be formatted to support cryptography, a capability described in more detail below. 
* Credential inspectors ensure the request is framed in such a way as to enable selective disclosure, using the cryptographic tools required. 

Once data minimization policies and selective disclosure are in place, the third and last enhancement can be applied. 

### Progressive Trust
Progressive trust is the process usable by an individual to gradually increase the amount of relevant data revealed as trust is built or value generated. 

To enable progressive trust capabilities, stakeholders in the credentials ecosystem act in the following ways:
* Issuers format the credential(s) in such a way as to enable progressive trust. This may require issuing multiple, related, atomic sub-credentials. It also may require formatting the credential to support mathematical queries and cryptographic proofs. Finally, the issuer's data model may express how the various sub-credentials are related in a scenario involving progressive trust. 
* Inspectors ensure that requests are framed in such a way as to enable progressive trust. They structure the communication in order to to gradually escalate credential requests in order to enable a subject to progressively trust the inspector more and more, revealing the minimum data necessary to accomplish each step of the task or goal and revealing more and more as the mutual communication progresses. 

### Progressive Disclosure
Progressive disclusure is one of the possible ways for an individual to let the progressive trust with another party proceed. The individual starts anonymously and continues to provide information, recognizing that the more data is revealed, the more risk that the other would be able to correlate. But with the gained trust, the risk will be considered a benefit, instead.

To enable progressive disclosure capabilities, stakeholders in the credentials ecosystem act in the following ways:
* Issuers format the credential(s) in such a way as to enable progressive disclosure. This requires issuing credentials that can be selectively disclosed multiple times. Each presentation will share attributes that have not been shared before. 
* Inspectors ensure that requests are framed in such a way as to enable progressive trust. This requires presentation protocols that can link together some previously made presentations, forming a progressive trust instance.

## Technology Characteristics

In this section, we can develop a list of technical characteristics with regard to (anti-)correlation, and try to map how different technology choices can fulfill them.

TODO:
- Maybe better differentiate between disclosure of data on various levels: credential, identifier, protocol, environment ("ambient correlation"?). On which layer does the (anti-)correlation happen, e.g. does it happen in the cryptographic signature, or on the identifier (DID) layer, etc.?
- How does salting fit in? Who does the salting?
- How do "sub-holders" fit in?

Characteristics:
* **[C1]** The Holder can selectively disclose data with a Verifier.
* **[C2]** The Holder can selectively disclose data with a Verifier, without requiring interaction with the Issuer during the disclosure process.
* **[C3]** The Holder can selectively disclose data with different Verifiers, without requiring interaction with the Issuer during the disclosure process.
* **[C4]** The Holder can arbitrarily choose which subset of data to selectively disclose with Verifiers, without requiring interaction with the Issuer during the disclosure process.
* **[C5]** When disclosing data to multiple different Verifiers, the Verifiers cannot correlate the subject via any single, unique identifier.
      [//]: # (@ChristopherA: With Gordian, issuers can do to this, but not holders)
* **[C6]** When disclosing data to the same Verifier in multiple different interactions, the Verifier cannot correlate the subject via any single, unique identifier.
* **[C7]** The Verifier can verify that the Holder who is disclosing the data is the same entity that has received the data from an Issuer. *<-- Note: Maybe reference the work of the other RWoT11 group that's working on "holder binding"*
* **[C8]** The Holder can selectively disclose data that is derived (using predicates) from the original data they have received from an Issuer.
* Type(s) of crypthography used.
* ...

Choices:
* **[PLAINSIG]** Plain signatures: Ed25519Signature2020, RSA signatures, etc.
* **[SUBCREDS]** Issuing multiple, granular "sub-" credentials separately, which the Holder can then use as appropriate for interactions with Verifiers.
* **[SINGLEUSECREDS]** Issuing single-use "bearer" credentials intended to be used for a single interaction between a Holder and a Verifier.
* **[BBS2020-LD]** BBS+ Signatures 2020: https://w3c-ccg.github.io/ldp-bbs2020/
* **[BBS-SIG]** BBS Signature Scheme: https://identity.foundation/bbs-signature/draft-looker-cfrg-bbs-signatures.html
* **[ANONCREDS]** AnonCreds: https://anoncreds-wg.github.io/anoncreds-spec/
* **[SD-JWT]** SD-JWT: https://github.com/oauthstuff/draft-selective-disclosure-jwt
* **[JWP]** JWP: https://www.ietf.org/archive/id/draft-jmiller-jose-json-web-proof-00.html
* **[REDACTION2016-LD]** Redaction Signature Suite 2016: https://w3c-ccg.github.io/lds-redaction2016/
* **[MERKLE2021-LD]** Merkle Disclosure Proof 2021: https://w3c-ccg.github.io/Merkle-Disclosure-2021/
* **[COCONUT]** Coconut: https://arxiv.org/abs/1802.07344
* **[GORDIAN]** Gordian Envelopes: https://github.com/BlockchainCommons/Gordian/blob/master/Docs/Envelope-Tech-Intro.md
* **[GORDIAN-IETF]** IETF I-D (draft will be submitted December): https://blockchaincommons.github.io/WIPs-IETF-draft-envelope/draft-mcnally-envelope.html

Mapping of Characteristics and Technologies

| Technology      | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 |
| ---             | -- | -- | -- | -- | -- | -- | -- | -- | -- |
|PLAINSIG         | ✕ | ✕ | ✕ | ✕ | ✕ | ✕ | ✓ | ✕ | ? |
|SUBCREDS         | ✓ | ✓ | ✓ | ✕ | ✕ | ✕ | ✓ | ✕ | ? |
|SINGLEUSECREDS   | ✓ | ✕ | ✕ | ✕ | ✓ | ✓ | ✕ | ✕ | ? |
|BBS2020-LD       | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | ✓ | ✕ | ? |
|BBS-SIG          | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | ? |
|ANONCREDS        | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ? |
|SD-JWT           | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | ✓ | ✕ | ? |
|JWP *            | ✓ | ✓ | ✓ | (✓) | (✓) | (✓) | ✓ | (✓) | ? |
|REDACTION2016-LD | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | ✓ | ✕ | ? |
|MERKLE2021-LD    | ✓ | ✓ | ✓ | ✓ | ✕ | ✕ | ✓ | ✕ | ? |
|COCONUT          | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✕ | ✓ | ? |
|GORDIAN **       | ✓ | ✓ | ✓ | ✓ | (✓) | ? | ? | ? | ? |

\* C4, C5, C6, C8 depend on the underlying JSON Proof Algorithm (JPA)

\** C5 can be done by issuers, but not holders

# Use Cases
[//]: # (We'd like to have a progressive set of use case to demonstration desireable and undesirable correlation, but also avoid government focused scenarios -- in particular the overused over-21 mobile driver's license example )

These use cases focus at their core a single credential, presented by various parties to others, with desirable correlation as well as threats if undesirable correlation occurs. An actual decentralized system probably has more trust assurance, multiple credentials and more verifiable presentation metadata than what is discussed below.

Educational / Professional Use Case:

(Educational use will be replaced by: https://github.com/BlockchainCommons/Gordian/blob/master/Docs/Envelope-Use-Cases-Educational.md)

1) DREW ENTERS SCHOOL: Drew, a member of a minority, desires comprehensive training in a high-paid building construction specialty — plasma welding. He applies to Acme Professional School and is accepted. Drew offers the school a DID, and adds details to allow for others to authenticate Drew (such as a photo) in a Student ID. Students of Acme Professional School are qualified to receive government-backed students loans from Burton Bank — Drew applies, and receives a loan, and payments are deferred until he graduates.
    * TRANSACTIONS: Drew becomes a student of Acme Professional School. Drew receives Student ID. Drew applies for student loan and pays for schooling.
    * DESIRABLE CORRELATION: Drew desires to be correlated as a student to attend classes and events, get a loan to pay for classes, and misc. services such as get student discounts.
    * CORRELATION THREAT: ?
    
2) DREW GETS AN EDUCATIONAL CREDENTIAL: Drew (the subject) obtains comprehensive training from a Acme Professional School (the issuer) on plasma welding, including a number of advanced techniques along with a variety of workplace safety topics. The Acme Professional School issues an education credential to Drew (the subject & holder) with all of these details that confirm Drew has all the proper training and experience for this complex technical discipline.
    * TRANSACTIONS: Drew completes schooling. Drew finishes payment for schooling. Drew receives educational credential from Acme Professional School.
    * DESIRABLE CORRELATION: Drew desires to be correlated as a graduate of school and qualified for various job prospects.
    * CORRELATION THREAT: ?

3) EXAMPLE OF ELISION BY A DREW (SUBJECT/HOLDER): Drew applies for a plasma welding gig at Erickson's Construction, that requires a workplace safety certification. Drew (the subject & holder) offers their Acme Professional School educational credential with details on the training and safety certification (they desire correlation as being qualified for the job) without personal information (such as ethnic name, photo as they do not wish to be correlated as a minority).
    * TRANSACTIONS: Drew applies for employement with educational credential.
    * DESIRABLE CORRELATION: Drew desires to be correlated as a qualified graduate, and school is correlated as a legitmate accredited school. ((Elena suggest is that sometimes you might want to elide the school name, for instance if it was accredited but was based in eastern europe it might be discrimiated against.))
    * CORRELATION THREAT: Drew does not wish to be descriminated against for his family origin.((Elena: and / or where his school is based))

4) EXAMPLE OF PROGRESSIVE TRUST: Erickson's Construction offers Drew a temporary gig at a reasonable hourly rate, pending additional verification of identity at the workplace. Drew (subject & holder) presents his Acme Professional School (issuer) educational credential again, but this time with sufficient authentication (such as photo), so that Erickson's Construction (verifier) can issue a pass to enter the workplace.
    * TRANSACTIONS: Drew receives offer, accepts offer. Gives tax information, legal name, photo. 
    * DESIRABLE CORRELATION: This same credential that the job was offered to is presented again, with less elision, to satisify workplace require.
    * CORRELATION THREAT: 

5) EXAMPLE OF ELISION BY A HOLDER: Erickson's Construction (holder≠subject) needs to prove to Three Towers Corporation (verifier) that they have sufficient number of qualified employees for the contract. They provide Drew's (subject) educational credential (already partially elided by the subject) to Three Towers. Three Towers can verify that the school (Acme Professional School but elided by Drew) is accredited, but in addition Erickson Construction further elides the names of the employees (to prevent pouching by other firms). 
    * TRANSACTIONS: Erickson Constructions (holder) presents Three Towers (verifier) multiple elided employee educational credentials.
    * DESIRABLE CORRELATION: Erickson Construction proves sufficent and properly trained staffing, from accredited schools.
    * CORRELATION THREAT:  * CORRELATION THREAT: Erickson Construction may not wish to be digital known as hiring minorities. Erikson Construction does not employees be 
    * MISC. is there an insurance or courts elision use case beyond this that isn't substantially similar?

6) Acme Professional School desires to renew their relationship with Burton Bank to continue to be able to recive funds from government-backed students loans. To renew this relationship Burton Bank & government regulators require that 80% of students complete a certificate within two years. Acme Professionalal School presents the educational credentials of their graduates, with all personal data elided. (As Burton Bank already has these personal data from all loan holders, they are not essential to the business purpose). Burton Bank is able to correlate which of those credentials hold loans, but not recieve toxic data of other students.
    * TRANSACTIONS: Acme Professional School (issuer) presents Burton Bank (verifier) multiple elided student educational credentials. Drew (subject) educational creditional is one of those credentials.
    * DESIRABLE CORRELATION: Both Acme Professional School and Burton Bank need to correlate educational credentials with loan holders.
    * CORRELATION THREAT: Burton Bank wants to avoid accepting correlatable personal data of non-loan  students, nor wishes to offer the personal data of students with loans.

[//]: # (@ChristopherA: I'm not sure about this last use case. It is accurate, but isn't as closely focused on elision of the educational credential. The bank knows a lot more about the students and likely can correlate at lot more details about students given minimal information from Acme. If we remove it, we probably should remove the student loan part of part 1.)

Healthcare Use Case:

1) ALEX STARTS A NEW JOB: Alex starts a new job at Company A and provides the Human Resources team at his new company with a DID to be issued an employment credential with his job positions and further details.
    * TRANSACTIONS: Alex becomes an employee of Company A and is issued an employment credential.
    * DESIRABLE CORRELATION: Alex wants to be correlated as an employee of Company A so he can get access to pension plans, employees' benefits and so on.
    * CORRELATION THREAT: 

2) ALEX SIGNS UP FOR A HEALTHCARE SERVICE PROVIDER: Alex joins a new healthcare provider. Alex can get a discount as an employee of Company A, due to a partnership between his company and the healthcare service provider. Alex presents his employment credential to the provider with eluded data.
    * TRANSACTIONS: Alex uses his employee credential to prove he works at Company A but eludes any irrelevant personal information.
    * DESIRABLE CORRELATION: Alex wants to be correlated as an employee of Company A.
    * PRIVACY THREAT: Alex does not want to share any further personal information with his new healthcare provider.

3) COMPANY A WANTS TO CHECK ON THE PARTNERSHIP WITH HEALTHCARE PROVIDER: Company A wants to check if the partnership with the healthcare provider is worth it to continue investing in. Company A requests data from the healthcare provider on the use of their employees.
    * TRANSACTIONS: Company A requests a data report from the healthcare provider.
    * DESIRABLE CORRELATION: Healthcare provider wants to correlate clients with employees of Company A.
    * CORRELATION THREAT: Healthcare provider does not want to accidentally reveal private information of their clients to Company A; Healthcare provider wants to only send data related to employees of Company A.

4) HEALTHCARE PROVIDER SENDS INFORMATION BACK TO COMPANY A: In order to reduce correlation, the healthcare provider decides to send back only the number of credentials with eluding of the employee's name so they can be sure to preserve privacy.
    * TRANSACTIONS: Healthcare provider sends data back to Company A with the number of clients that are verified employees of Company A without showing their names.
    * DESIRABLE CORRELATION: Healthcare provider correlates clients to be employees of Company A.
    * ELISION BY HOLDER: Healthcare provider eludes the names of the clients.
    
## Correlation Adversary from Smart Custody

![](https://github.com/BlockchainCommons/SmartCustodyBook/raw/master/manuscript/resources/image3.png)

***Motivation.** “I want information. I watch cryptocurrency transactions with an eagle eye, ready to swoop in on any mistake. If you keep making the same payments or receiving the same payments or using the same addresses, I’ll figure it out. I want to connect the dots to determine who is spending cryptocurrency for what, and I can figure that puzzle out if you give me enough pieces.”*

***Key Words:** Active, Technological.*

Cryptocurrency use is pseudo-anonymous and somewhat private. However, it’s not totally so: it’s possible to build up correlation. Through statistical analysis and through the discovery of accidental revelations, a third-party could tie together an asset holder’s usage of various funds to paint a larger picture of their finances and contacts.

***Abstract Case Study: Correlating over Coffee.*** Alice is sloppy with her bitcoins and tends to use one address for everything. She goes out to buy a coffee with bitcoins; while she sips away at the café, working at her laptop, the barista notes the huge number of bitcoins going into the address. She follows Alice home, planning larceny.

***Abstract Case Study: Correlating Identities.*** Carol uses the same online identity on bitcointalk and on twitter. Eastern European hackers monitor twitter, see her talking about bitcoins, track that back to bitcointalk, and find wallet addresses mentioned there that reveal her bitcoin wealth. They then set their scripts lose, hoping to break into her computer and steal her keys.

{pagebreak}

***Risks:***

1.  **Funds Revelation.** An asset holder’s ownership of various funds can be revealed. This can make it possible to track what they spent funds on and who they’re associated with. It also puts them in greater danger from any number of other adversaries.
2.  **Cascade: Censorship.** If they know who you are, they can block you.
3.  **Cascade: Coercion.** If they know who you are, they can threaten you.
4.  **Cascade: Legal Forfeiture.** If they know who you are, you can become a target for a nation-state or for a civil action.
5.  **Cascade: Loss of Fungibility.** If funds have been correlated, they may lose fungibility.
6.  **Cascade: Sophisticated Theft.** If they know who you are, you can become a target for thieves.

***Process Solutions:***

1.  **Practice Anonymity.** Do not let people know you have bitcoins; ensure that you in no way ever link your key to your real persona.
2.  **Practice Anonymizing Your Funds.** Occasionally use methods like CoinJoin, SendShared, or Zerocoin to anonymize your transactions. On Blockstream’s Liquid, always make use of Confidential Transactions.
3.  **Practice Key Hygiene.** Follow the best practices of using different addresses for every transaction that you conduct. Each time you make a transaction with the same address, you are leaking information to your counterparty, which could be used to identify and either censor or correlate future transactions.


# Conclusion/Summary
TODO

[//]: # (We encourage policy makers to be awesome and do good, not evil.)

# Acknowledgements

We would like to acknowledge the following persons who contributed ideas that helped define the ideas in and the shape of this paper:
* Adrian Gropper
* Joe Andrieu

We also acknowledge the contributors to [the 2019 paper](https://w3c-ccg.github.io/data-minimization/):

* Lionel Wolberger, Platin.io
* Brent Zundel, Evernym/Sovrin
* Zachary Larson, Independent
* Irene Hernandez, Independent 
* Katryna Dow, Meeco
* Christohper Allen, Blockchain Commons

# References

## Related RWOT Advance Readings & Topics
* [Elision, Redaction, and Noncorrelation in Smart Documents](https://github.com/WebOfTrustInfo/rwot11-the-hague/blob/master/advance-readings/elision-redaction-correlation-smart-documents.md)
* [Reducing Correlation: To What Degree is it Necessary?](https://github.com/WebOfTrustInfo/rwot11-the-hague/blob/master/advance-readings/reducing-correlation.md)

## Links to other related documents
* [Engineering Privacy for Verified Credentials: In Which We Describe Data Minimization, Selective Disclosure, and Progressive Trust](https://github.com/w3c-ccg/data-minimization) (last updated Apr 12, 2022, released by W3C-CCG Apr 4, 2019)
* [Formalising Linked-Data based Verifiable Credentials for Selective Disclosure](https://ssr2022.com/slides/FormalisingLinkedDataBasedVerifiableCredentials.pdf)
* Recent PRs on [VC Data Integrity spec](https://w3c.github.io/vc-data-integrity/) in [W3C VC WG](https://www.w3.org/groups/wg/vc):
    * About unlinkability: https://github.com/w3c/vc-data-integrity/pull/52
    * About selective disclosure: https://github.com/w3c/vc-data-integrity/pull/53
* [Progressive Trust](http://www.lifewithalacrity.com/2004/08/progressive_tru.html) From Life With Alacrity by Christopher Allen 2004
* [W3C Credentials Community Group Home Page](https://www.w3.org/community/credentials/)
* Camenisch, Lysyanskaya. [An Efficient System for Non-transferable Anonymous Credentials with Optional Anonymity Revocation](http://www.cs.ru.nl/~jhh/pub/secsem/camenish2001idemix-clean.pdf)
* Pfitzmann, Hansen. 2010. [A terminology for talking about privacy by data minimization: Anonymity, Unlinkability, Undetectability, Unobservability, Pseudonymity, and Identity Management](https://www.researchgate.net/publication/234720523_A_terminology_for_talking_about_privacy_by_data_minimization_Anonymity_Unlinkability_Undetectability_Unobservability_Pseudonymity_and_Identity_Management)
* Cooper, Tschofenig, Aboba, Peterson, Morris, Hansen, Smith, Janet. 2013. [RFC6973](https://tools.ietf.org/html/rfc6973). The draft can also be helpful, "This document focuses on introducing terms used to describe privacy properties that support data minimization." 
* Hansen, Tschofenig, Smith, Cooper. 2012 [Privacy Terminology and Concepts. Network Working Group Internet-Draft Expires: September 13, 2012](https://tools.ietf.org/html/draft-iab-privacy-terminology-01)

## Citations
[^1]: [Identity Crisis: Clearer Identity Through Correlation](https://github.com/WebOfTrustInfo/ID2020DesignWorkshop/blob/master/final-documents/identity-crisis.pdf)
[^2]: [Verifiable Credentials Data Model](https://www.w3.org/TR/vc-data-model/)
[^3]: [OpenID for Verifiable Credential Issuance](https://openid.net/specs/openid-4-verifiable-credential-issuance-1_0.html)
[^4]: [DIDComm Messaging Specification](https://identity.foundation/didcomm-messaging/spec/)