During the last decade, all segments of society have become increasingly alarmed by the amount of data, and resulting power, held by a small number of actors . Data is, by some, famously called “the new oil”  and comes from records of the behavior of citizens. Why then, is control of this powerful new resource concentrated in so few hands? During the last 150 years, questions about concentration of power have emerged each time the economy has shifted to a new paradigm; industrial employment replacing agricultural employment, consumer banking replacing cash and barter, and now ultra-efficient digital businesses replacing traditional physical businesses and civic systems.
As the economy was transformed by industrialization and then by consumer banking, powerful new players such as Standard Oil, J.P. Morgan, and a handful of others threatened the freedom of citizens. In order to provide a counterweight to these new powers, citizens joined together to form trade unions and cooperative banking institutions, which were federally chartered to represent their members’ interests. These citizen organizations helped balance the economic and social power between large and small players and between employers and worker.
The same collective organization is required to move from an individualized asset-based understanding of data control to a collective system based on rights and accountability, with legal standards upheld by a new class of representatives who act as fiduciaries for their members. In the U.S. almost 100 million people are members of credit unions, not-for-profit institutions owned by their members, and already chartered to securely manage their members? Digital data and to represent them in a wide variety of financial transactions, including insurance, investments, and benefits. The question then is, could we apply the same push for citizen power to the area of data rights in the ever-growing digital economy?
Indeed, with advanced computing technologies it is practically possible to automatically record and organize all the data that citizens knowingly or unknowingly give to companies and the government, and to store these data in credit union vaults. In addition, almost all credit unions already manage their accounts through regional associations that use common software, so widespread deployment of data cooperative capabilities could become surprisingly quick and easy.
Data Cooperatives as Citizens’ Organizations
The notion of a data cooperative refers to the voluntary collaborative pooling by individuals of their personal data for the benefit of the membership of the group or community. The motivation for individuals to get together and pool their data is driven by the need to share common insights across data that would be otherwise siloed or inaccessible. These insights provide the cooperative members as a whole with a better understanding of their current economic, health and social conditions as compared to the other members of the cooperative generally.
It is technically and legally straightforward to have credit unions hold copies of all their members’ data, to safeguard their rights, represent them in negotiating how their data is used, to alert them to how they are being surveilled, and to audit the companies using their members’ data. The power of 100 million US consumers who are practically and legally in control of their data would be a force to be reckoned with by all organizations that use citizen data and would be one very decisive way to hold these organizations accountable. The same potential for credit unions to balance today’s data monoliths exists in most countries around the world.
It is critical to note that credit unions and similar organizations have fiduciary responsibilities to protect the sensitive information that is shared by members, as this is a central element in bringing data rights to the membership. As a consequence, members will gain privacy, transparency, and empowerment as to data use and can direct the use to their collective best benefit as they see fit.
Who will lead this historic, and necessary transformation? The answer could well be credit unions, many of which are directly associated with universities, city governments, trade unions, and the like. They are chartered to represent their members in transactions related to their employment.
The ability to balance the world’s data economy depends on creating a balance of stakeholders. Today citizens and workers have no direct representation at the negotiation table, and so lose out. By leveraging cooperative worker and citizen organizations that are already chartered in law virtually everywhere in the world, along with existing technology, we can change this situation and create a sustainable digital economy that serves the many, and not just the few.
A case in point would be the income made by ride-share automobile drivers (e.g. Uber, Lyft, etc.). Currently, it is difficult for drivers to compare their respective incomes across similar routes, areas and distances. Similarly, passengers or riders who use these ride-share services have little or no idea whether their average fees for a given distance and city-sector is comparable to similar situations in a different sector of the city. By pooling together data accessible to them on their devices, drivers and passengers are able to see whether on average they are obtaining similar quality of service and equitable payment across a wide geographic area.
There are several key aspects to the notion of the data cooperative:
Individual members own and control their personal data: The individual as a member of the data cooperative has unambiguous legal ownership of (the copies of) their data. Each member can collect copies of their data through various means, either automatically using electronic means (e.g. passive data-traffic copying software on their devices) or by manually uploading data files to the cooperative. This data is collected into the member’s personal data store (PDS) . The member is able to add, subtract or remove data from their personal data store, and even suspend access to their data store. A member may possess multiple personal data repositories.
The member has the option to maintain their personal data store at the cooperative, or host it elsewhere (e.g. private data server, cloud provider, etc.). In the case where the member chooses to host the personal data store at the cooperative, the cooperative has the task of protecting the data (e.g. encryption for data loss prevention) and optionally curating the data sets for the benefit of the member (e.g. placing into common format, providing informative graphical reporting, etc.).
Fiduciary obligations to members: The data cooperative has a legal fiduciary obligation first and foremost to its members . The organization is member-owned and member-run, and it must be governed by rules (bylaws) agreed to by all the members.
A key part of this governance rules is to establish clear policies regarding the usage or access to data belonging to its members. These policies have direct influence on the work-flow of data access within the cooperative’s infrastructure, which in turn has impact on how data privacy is enforced within the organization.
Direct benefit to members: The goal of the data cooperative is to benefit its members first and foremost. The goal is not to “monetize” their data, but instead to perform on-going analytics to understand better the needs of the members and to share insights among the members.
There are numerous ways for a data cooperative to provide value to its members. For example, the cooperative could perform data analysis related to the health and age of its members, based for example on location data-sets. It may find, for instance, that a certain subset of the membership is not sufficiently paying attention to their health (e.g. not using available medical services). In such cases, the cooperative could devise strategies to remedy the situation, such as intervening and/or negotiating with external providers for better service rates (e.g. discounts to sporting facilities, health services, etc.). Thus, these insights provide the cooperative with better bargaining power when it negotiates group purchases.
The Data Cooperative Ecosystem
The data cooperative ecosystem is summarized in Figure 1. The main entities are the (i) data cooperative as a legal entity, (ii) the individuals who make-up the membership and elect the leadership of the cooperative, and (iii) the external entities who interact with the data cooperative, referred to as queriers.
The cooperative as an organization may choose to operate its own IT infrastructure or choose to outsource these IT functions to an external operator or IT services provider. In the case of outsourcing, the service level agreement (SLA) and contracts must include the prohibition for the operators to access or copy the members data. Furthermore, the prohibition must extend to all other third-party entities from which the outsourcing operator purchases or subcontracts parts if its own services.
A good analogy can be gleaned from Credit Unions throughout the United States. Many of the small credit unions band together to share IT costs by outsourcing IT services from a common provider, known in industry as Credit Union Service Organizations (CUSO). Thus, a credit union in Vermont may band together with one in Texas and another in California, to contract a CUSO to provide basic IT services. This includes a common computing platform on the cloud, shared storage on the cloud, shared applications, and so on. The credit union may not have any equipment on-premises, other than the PC computers used to connect to the platform operated by the CUSO. Here, despite the three credit unions using a common platform the CUSO may tailor the appearance of the user interface differently for each credit union in order to provide some degree of differentiation to its members. However, the CUSO in turn may be subcontracting functions or applications from a third party. For example, the CUSO may be running its platform using virtualization technology on Amazon Web Services (AWS). It may purchase storage from yet a different entity. This approach of subcontracting functions or services from other service provider is currently very common.
In the context of the data cooperative that choses to outsource IT services, the service contract with the IT services provider must include prohibitions by third party cloud providers from accessing data belonging to the cooperative’s members.
Preserving Data Privacy of Members
We propose to use the MIT Open Algorithms (OPAL) approach to ensure the privacy of the member’s data held within the personal data stores. In essence, the OPAL paradigm requires that data never be moved or be copied out of its data store, and that the algorithms are instead transmitted to the data stores for execution.
The following are the key concepts and principles underlying the open algorithms paradigm :
Move the algorithm to the data: Instead of “pulling” data into a centralized location for processing, it is the algorithm that must be transmitted to the data repositories endpoints and be processed there.
Data must never leave its repository: Data must never be exported or copied from its repository. Additional local data-loss protection could be applied, such as encryption (e.g. homomorphic encryption) to prevent backdoor theft of the data.
Vetted algorithms: Algorithms must be vetted to be “safe” from bias, discrimination, privacy violations and other unintended consequences.
Provide only safe answers: When returning results from executing one or more algorithms, return aggregate answers only as the default granularity of the response.
Aggregate responses must be granular enough so that it does not allow the recipient (e.g. querier entity) to perform correlation attacks that re-identify individuals. Any algorithm that is intended to yield answers that are specific to a data subject (individual) must only be executed after obtaining the subject’s affirmative and fully informed consent .
Consent for Algorithm Execution
One of the contributions of the EU GDPR regulation  is the formal recognition at the regulatory level for the need for informed consent to be obtain from subjects. More specifically, the GDPR calls for the ability for the entity processing the data to
...demonstrate that the data subject has consented to processing of his or her personal data (Article 7).
Related to this, a given
...data subject shall have the right to withdraw his or her consent at any time (Article 7).
In terms of minimizing the practice of copying data unnecessarily, the GDPR calls out in clear terms the need to access data
...limited to what is necessary in relation to the purposes for which they are processed (data minimisation) (Article 5).
In the context of the GDPR, we believe that the MIT Open Algorithms approach substantially addresses the various issues raised by the GDPR by virtue of data never being moved or copied from its repository.
Furthermore, because OPAL requires algorithms to be selected and transmitted to the data endpoints for execution, the matter of consent in OPAL becomes one of requesting permission from the subject for the execution of one or more vetted algorithms on the subject’s data. The data cooperative as a member organization has the task of explaining in lay terms the meaning and purpose of each algorithm, and convey to the members the benefits from executing the algorithm on the member’s data.
In terms of the consent management system implementation by a data cooperative, there are additional requirements that pertain to indirect access by service providers and operators that may be hosting data belonging to members of the cooperative. More specifically, when an entity employs a third-party operated service (e.g. client or application running in the cloud) and that service handles data, algorithms and computation results related to the cooperative’s activities, then we believe authorization must be expressly obtained by that third-party.
In the context of possible implementations of authorization and consent management, the current popular access authorization framework used by most hosted application and services providers today is based on the OAuth2.0 authorization framework . The OAuth2.0 model is relatively simple in that it recognizes three (3) basic entities in the authorization work-flow. The first entity is the resource-owner, which in our case translates to the cooperative on behalf of its members. The second entity is the authorization service, which could map to either the cooperative or an outsourced provider. The third entity is the requesting party using a client (application), which maps roughly to our querier (person or organization seeking insights). In the case that the data cooperative is performing internal analytics for its own purposes, then the querier is the cooperative itself.
While the OAuth2.0 model has gained traction in industry over the past decade (e.g. in mobile apps), its simplistic view of the 3-party world does not take into account the reality today of the popularity of hosted applications and services. In reality the three parties in OAuth2.0 (namely the client, the authorization server and the resource) could each be operated by separate legal entities. For example, the client application could be running in the cloud, and thus any information or data passing through the client application becomes accessible to the cloud provider.
An early awareness of the inherent limitations of OAuth2.0 has led to additional efforts to be directed at expanding the 3-party configuration to become a 5 or 6 party arrangement (Figure 3), while retaining the same OAuth2.0 token and messaging formats. This work has been conducted in the Kantara Initiative standards organization since 2009, under the umbrella of User Managed Access (UMA) . As implied by its name, UMA seeks to provide ordinary users as resource (data) owners with the ability to manage access policy in a consistent manner across the user’s resources that maybe physically distributed throughout different repositories on the Internet. The UMA entities follows closely and extends the entities defined in the OAuth2.0 framework. More importantly, the UMA model introduces new functions and tokens that allow it to address complex scenarios that explicitly identity hosted services providers and cloud operators as entities that must abide by the same consent terms of service:
Recognition of service operators as 3rd party legal entities: The UMA architecture explicitly calls-out entities which provide services to the basic OAuth2.0 entities. The goal is to extend the legal obligations to these entities as well, which is crucial for implementing informed consent in the sense of the GDPR.
Thus, for example, in the UMA work-flow in Figure 3, the client is recognized to be consisting of two separate entities: the querier (e.g. person) that that operates the hosted client-application, and the Service Provider A that makes available the client-application on its infrastructure. When the querier is authenticated by the authorization server and is issued an access-token, the Service Provider A must also be separately authenticated and be issued a unique access token.
This means that Service Provider A which operates the client-application must accept the terms of service and data usage agreement presented by the authorization server, in the same manner that the querier (person or organization) must accept them.
Multi-round handshake as a progressive legal binding mechanism: Another important contribution of the UMA architecture is the recognition that a given endpoint (e.g. API at the authorization server) provides the opportunity to successively engage the caller to agree to a terms of service and data usage agreement (referred to as binding obligations in UMA).
More specifically, UMA uses the multi-round protocol run between the client and the authorization server to progressively bind the client in a lock-step manner. When the client (client-operator) chooses to proceed with the handshake by sending the next message in the protocol to the endpoint of the authorization server, the client has implicitly agreed to the terms of service at that endpoint. This is akin to the client agreeing step-by-step to additional clauses in a contract each time the client proceeds with the next stage of the handshake.
Identity-related Algorithmic Assertions
A potential role for a data cooperative is to make available the summary results of analytic computations to external entities regarding a member (subject) upon request by the member. Here the work-flow must be initiated by the member who is using his or her data (in their personal data store) as the basis for generating the assertions about them, based on executing one or more of the cooperative-vetted algorithms. In this case, the cooperative behaves as an Attribute Provider or Assertions Provider for its members , by issuing a signed assertion in a standard format (e.g. SAML2.0  or Claims ). This is particularly useful when the member is seeking to obtains goods and services from an external Service Provider (SP).
As an example, a particular member (individual) could be seeking a loan (e.g. car loan) from a financial institution. The financial institution requires proof of incomes and expenditures regarding the member over a duration of time (e.g. last 5 years), as part of its risk assessment process. It needs an authoritative and truthful source of information regarding the member’s financial behavior over the last 5 years. This role today in the United States is fulfilled by the so called credit scoring or credit report companies, such as Equifax, TransUnion and Experian.
However, in this case the member could turn to its cooperative and request the cooperative to run various algorithms – including algorithms private to the cooperative – on the various data sets regarding the member located in the member’s personal data store. At the end of these computations the
cooperative could issue an authoritative and truthful assertion, which it signs using its private-key. The digital signature signifies that the cooperative stands behind its assertions regarding the given member. Then the cooperative or the member could transmit the signed assertion to the financial institution. Note that this cycle of executing algorithms, followed by assertions creation and transmittal to the financial institution can be repeated as many times as needed, until the financial institution is satisfied.
There are a number of important aspects regarding this approach of relying on the data cooperative:
Member driven: The algorithmic computation on the member’s data and the assertion issuance must be invoked or initiated by the member. The data cooperative must not perform this computation and issue assertions (about a member) without express directive from the member.
Short-lived assertions: The assertion validity period should be very limited to the duration of time specified by the service provider. This reduces the window of opportunity for the service provider to hoard or re-sell to a third party the assertions obtained from the cooperative.
Limited to a specific purpose: The assertion should carry additional legal clause indicating that the assertion is to be used for a given purpose only (e.g. member’s application for a specific loan type and loan amount).
Signature of cooperative: The data cooperative as the issuer of the assertions or claims must digitally sign the assertions. This conveys the consent of the member (for the issuance of assertion) and conveys the authority of the cooperative as the entity who executes algorithms over the member’s data.
Portability of assertions: The assertion data structure should be independent (stand-alone), portable and not tied to any specific infrastructure.
In other cases, a member may wish for some types of assertions containing static personal attributes (e.g. age or year of birth) to be readily available without the privacy limitations. For example, the member might use such attribute-based assertions to purchase merchandise tied to age limits (e.g. alcohol). In this case, the signed assertion can be readable from a well-known endpoint at the cooperative, be readable for the member’s personal website, or be carried inside the member’s mobile device. Hence the importance of the portability of the assertions structure.
Today we are in a situation where individual assets ...people’s personal data... is being exploited without sufficient value being returned to the individual. This is analogous to the situation in the late 1800’s and early 1900’s that led to the creation of collective institutions such as credit unions and labor unions, and so the time seems ripe for the creation of collective institutions to represent the data rights of individuals. This is analogous to the situation in the late 1800’s and early 1900’s that led to the creation of collective institutions such as credit unions and labor unions, and so the time seems ripe for the creation of collective institutions to represent the data rights of individuals.
We have argued that data cooperatives with fiduciary obligations to members provide a promising direction for the empowerment of individuals through collective use of their own personal data. Not only can a data cooperative give the individual expert, community-based advice on how to manage, curate and protect access to their personal data, it can run internal analytics that benefit the collective membership. Such collective insights provide a powerful tool for negotiating better services and discounts for its members. Federally chartered Credit Unions are already legally empowered to act as data cooperatives, and we believe that there are many other similar institutions that could also provide data cooperative services.
- “Not only can a data cooperative give the individual expert, community-based advice on how to manage”
+ “Not only can a data cooperative give the individual expert community-based advice on how to manage”
+ “may be”
Thoughts on the account aggregator model that’s emerging in India?
Particularly if the insights are mediated by a 3rd party service provider. Unions might otherwise baulk at reputation risk and legal liability for actions taken obo membership. Some rechartering may mitigate these concerns.
correction: “members’ data”
One could posit this is also a cheaper faster way to comply even with CCPA: The California Attorney General’s Office estimates that CCPA compliance’s total cost to California businesses will start at $55 billion initially, then cost anywhere from $467 million to $16 billion over the next 10 years.
Credit unions are poised to shoulder a significant share of these costs, as they will require an extensive review of every megabyte of personal data they process to ensure they remain compliant. They will also be required to create new internal security procedures and invest in staff training to avoid violation risks.
PYMNTS’ Credit Union Innovation Index found 65 percent of CU members chose credit unions as their primary financial institutions (FIs) because they trusted them, compared to 45 percent of non-CU members who said the same. It also revealed that 60.8 percent of the former said they would not leave their CUs for other FIs even if offered the same financial services — an indicator of how important trust is in influencing members’ decisions.