WorkPi (Compute to SSI) - OceanDAO Round 10

Progress update

In round 9, WorkPi was granted for the first proposal within the Ocean DAO. Thank you all very much for the support. I’m very pleased to announce that all deliverables have been achieved. We were also able to do some additional background research that was not mentioned in the previous proposal. Before we dive into the proposal for round 10 we have summarised the progress so far:

Extra delivered next to proposed deliverables: :white_check_mark:

General knowledge base on SSI wallets, federated learning & Privacy-preserving AI. While doing the research, we realised that obtaining background information on different types of federate learning models and privacy-preserving AI models is necessary for readers to understand our research. Naturally, we included this in our documentation.

Background documentation:

Deliverable 1: :white_check_mark:

Create an architecture on how the Compute-To-Data technology could be applied on SSI wallets.

Currently Ocean’s compute-to-data uses an architecture that involves kubernetes clusters where a visiting docker container contains the machine learning algorithm and kubernetes configurations allowing it access to (portions) of the data. A new method has to be devised for SSI Wallets as they are typically running on mobile phones which do not allow for apps to run kubernetes server clusters in a usable fashion.


Deliverable 2: :white_check_mark:

Create a knowledge base with the precise definition of Compute-To-SSI, it’s purpose, architecture (including diagrams) and the way to create software using it. This will be set up as a public git repository and will track further progress of the project as well as allow anyone to participate/contribute.

Compute on SSI Wallets:

Deliverable 3: :white_check_mark:

Model how SSI wallets would interact with the Ocean Protocol ecosystem, i.e. rent existing machine learning models, how to pay for it, or be paid for it. This will naturally be added to the knowledge base.

This deliverable is mostly woven into the rest of the text. Most details can be found in the 2 subparts mentioned, but in order to acquire all information on this deliverable, one must read the entire document.

Deliverable 4: :white_check_mark:

Model how an interested party can utilize the network of SSI wallets to train their machine learning algorithm for a specific purpose. Define what taxonomies are required. This will naturally also be added to the knowledge base.

Example use-case:

Please find the entire knowledge base here:

Part 1 - Proposal Submission

Name of Project

Work X - Compute-To-SSI

Proposal in one sentence

Use Compute-To-Data algorithms on data enclosed in personal Self-Sovereign Identity (SSI) wallets without compromising privacy, we call this Compute-To-SSI.

Description of the project and what problem is it solving

While personal data being protected and governed with SSI wallets can guarantee privacy of that data, currently A.I. algorithms are not able to learn from it. This is a large barrier for adoption. Furthermore we find that the companies that do want to share data using Self Sovereign Identity, prefer to keep their own copy. In some cases like governments this is a necessity, however in many situations it is not.

If private personal data would only exist in SSI wallets, using Compute-To-SSI would enable companies not only to benefit from reduced GDPR risk, it would allow them to learn from this personal data without having to possess it.

Historically, enterprises are very hesitant to share data with other companies, due to privacy concerns and ultimately losing their competitive advantage. This cripples their ability to train an accurate A.I. and gather valuable insights. Besides machine learning algorithms still being able to learn from the data given back to the individual, it could also learn from the data provided by other companies/entities. A Compute-To-SSI mechanism could enable a data driven revolution, as it would grant access to an enormous decentralized pool of data to learn from. By doing so, this will greatly increase the accuracy of these algorithms.

We feel this research area has the potential to let people and organisations benefit from Artificial Intelligence models, which are now exclusively available to tech giants, while preserving privacy.

At WorkPi we are building a decentralized Internet of Jobs, and provide a SaaS solution to enterprises to assess and educate their employees. By doing so, we are creating one of the many valuable use cases that will be enabled by the compute-to-SSI model. After all, besides skills & education information, there are many other types of data that a person can add to their personal data wallet (e.g. health, wearable, cookies, identity or finance data).

This solution will feature the ability to export and import your career related profile to an SSI wallet. In order to become compliant and future proof we are participating in the eSSIF* program of the European Commission, and integrate with the EBSI blockchain**


We strongly believe in giving back personal data to individuals. We see this as a win-win for both the individual as well as enterprises and the key to both fair economic and personal success. However, at this point the problem remains how to learn from this private data.

We decided to work with Ocean Protocol as they have presented innovative solutions, with a strong focus on retaining privacy of data in large silo’s (e.g. hospitals). We aim to take this one step further; directly into the pockets of the people. People will be able to easily give access to specific data as well as revoke it. At the same time, A.I. algorithms will get the ability to learn from this data, all while preserving privacy.

While this will be no easy feat to accomplish, all technical components required to achieve this goal already exist. At WorkPi we are prepared to embark on this grand voyage of the world’s Ocean with you, to improve the ecosystem.

In order to succeed, this project has to be planned carefully and architected precisely with secured partners in the ecosystem.

We want to be very clear about the fact that from the beginning a series of grants is required before we can realize the end goal. An open-source reference implementation that any wallet can use to enable any kind of machine learning on any type of private data, for any purpose they desire. WorkPi will be amongst the first companies to use this in conjunction with the European Blockchain for career related data.

Grant Deliverables Round 10:

Deliverable 1:

Since we want to be absolutely sure that what we are building is the right approach, we want to validate our architecture with external parties. In order to do this alliances have been forged with machine learning, edge computing and security specialists while more are being under negotiation as we speak.

Validate the architecture with experts in the following fields:

  • Data Science
  • Security
  • Privacy
  • Edge computing

Deliverable 2:

The Compute-To-SSI architecture has to fit well within the wallet software, to ensure this we need to firstly identify a set of open source wallets that are compatible with EBSI and have sufficient adoption to succeed. And then align with the wallet dev-teams on how to implement receiving ocean algo’s and training them, to see if our architecture is the most optimal solution, and implement possible improvements.

Which category best describes your project?

Build / improve applications or integrations to Ocean

Which Fundamental Metric best describes your project?

Consumer Volume: Compute-To-SSI enables machine learning algorithms to learn from private data that is currently not accessible. This would significantly increase the consumer volume. Also, the open-source reference implementation can be used by other projects to apply machine learning on private data driving adoption further.

What is the final product?:

Being able to learn from data residing in SSI wallets that are compliant with European legislation. This means the highest standard of privacy must be implemented.

How does this project drive value to the “fundamental metric” (listed above) and the overall Ocean ecosystem?

There will be at least one new dataset listed which contains the career information of the dataset participants, stored in their SSI wallets. This data will be consumed by a lot of algorithms to generate statistics and machine learning algorithms on top of this data.

To prove and quantify the added value of Compute-To-SSI for the Ocean Ecosystem we have created four different calculations:

  1. Value created & validated by WorkPi with a career data use-case
  2. Total potential value across other use-cases and entities within the eSSIF ecosystem.

1. Value created & validated by WorkPi with a career data use-case

Using WorkPi, people gather career-related data about their skills, characteristics & preferences with assessments or development courses. This type of information can be compared to a LinkedIn profile (although it will be a lot richer because the user is stimulated to track their detailed achievements due to the privacy friendliness).

In 2015 an article revealed that the value of a LinkedIn profile when selling, equals $0,40 while it is being re-sold for ~$200. This means that the intermediary is earning a commission fee of $199,60 which equals an astonishing 49900%. To put this in perspective: currently Apple is under fire for charging a 30% fee in their App Store.

In our calculation we decided to pick an average selling-value of $100, a price that matches the value that we personally received on conferences to share our LinkedIn profile with prominent parties doing data analytics.

Since the buyer does not purchase full access to the profile, but instead only the right to train their A.I. algorithm on the data, we have to take 80% of the profile value because we estimate 60% of the times data is accessed, it is to train an A.I. algorithm. This brings the value of one profile, for machine learning purposes only, to: 0,6 * $100 = $60

WorkPi is currently in the last phase of building the application for our launching client; a large finance company in The Netherlands with ~3.000 employees. Based on this product we are building a SaaS solution for ~40 interested enterprises (which have >1.000 employees in around 14 different industries). The final user group will be onboarded using Work X; our decentralised platform for work where job candidates can participate in the same assessments & development courses as employees. Work X will be launched late 2021 and we expect 2.000-5.000 users in the early phase of the project. Although we expect a large number of users from multiple sources, we are projecting a modest amount of 1.500 users in our calculation.

ROI calculation grants:

* Bang = 1.500 profiles * $60 = $90.000
* Buck = $ 20.000
* % chance of success = 80%

Expected ROI = $90.000 / $20.000 * 0,80 = 3,6

2. Total potential value across other use-cases and entities within the eSSIF ecosystem.

WorkPi will participate in an 8-month programme of eSSIF-Lab, supported and funded by the European Commission. Because of large involved governmental stakeholders in this ecosystem (e.g. Spain, Germany, The Netherlands, Belgium), the total potential upside of this grant proposal is very large. The integration with the eSSIF ecosystem is a large process that could take up to 7 rounds.

Please find the full cost forecast here:

  • The EU counts around 447 million citizens. In this calculation we project an early adoption rate of 5%.
  • About 86% of these people have access to internet (2018)
  • Although we expect that the value of the data that can be linked to an SSI wallet greatly exceeds that of just the career-related data (given the broad amount of data types), we also use a value of $60 dollars in this calculation.

ROI calculations:

* Bang = 447 million * 0.86 * $60 * 0.05 = $1.153 billion
* Buck = 6 * 20.000 = $120.000
* % chance of success = 70%

Expected ROI = $ / $120.000 * 0,70 = 6727 (yes, that’s a lot… we believe that this could be a billion dollar grant given the growing global importance of AI and data privacy).

Funding Requested:


Proposal Wallet Address


Have you previously received an OceanDAO Grant?


Team Website:

Twitter Handle:



Discord Handle:

Rik Rapmund#5093

Daniel de Witte#9812


Project lead Contact Email:

Country of Residence:

The Netherlands

Part 2 - Proposal Details

Mockups & Designs

Teaser pitch deck HR use-case:
WorkPi Teaser Pitch Deck _compressed.pdf (1.4 MB)


  • NodeJS
  • Typescript
  • React
  • Storybook
  • Adobe XD
  • GraphQL
  • Docker
  • Kubernetes
  • Azure
  • Self Sovereign Identity
  • Python
  • Solidity


  • Microservices Framework
  • Continuous Integration
  • Event Based Messaging
  • Flexible Data Model
  • Privacy Oriented Design
  • Recursion
  • Federated Server System
  • Isomorphic Rolebased Authorization

Project Deliverables - Roadmap

Beyond the scope of this grant proposal we have designed a roadmap to show the long term goal of the entire project:

  • Training with Zero Knowledge (For example Apple’s FaceID faced acceptance problems, even though only the weights of the trained model were sent back to the server)

  • Create a security model that can guarantee the privacy of an individual’s data while being visited by machine learning algorithms. (Zero Knowledge Proofs could help here)

  • Create a reference implementation in one of the chosen wallets.

  • Keep expanding wallet support to grow the ecosystem of wallets able to host Ocean machine learning algorithms.

  • Utilize A.I. chips in phones to speed up calculations, reduce energy consumption and keep your phone cool.

Team members

  1. Rik Rapmund
  1. Daniel de Witte
  1. Hidde Kehrer
  1. Steven van der Graaf
  1. Joseph Groot-Kormelink
  1. Niek Naber
  1. Darius Costolas - Full-stack developer

  2. Pavlik Kiselev - Quality Assurance

  3. Alex Basmanov - Front-End developer

  4. Tiberiu Paliuc - Graphic designer

  5. Kirill Ryadchin - Junior front-end developer

Additional Information

  • WorkPi participates in the eSSIF development trajectory from the European Commision to enable individuals to regain control over their work related data using Self Sovereign Identity.

  • WorkPi has an enterprise launching client called APG.

  • WorkPi is a member of the Dutch Blockchain Coalition.

  • WorkPi is an Odyssey 2020 hackathon winner in the SSI category.

  • WorkPi has been featured on Dutch national television explaining the project in detail


Hello team,

I’m not technically qualified to judge your work, but I enjoyed reviewing your proposal and ideas.

The following additions would help me to make my decision and vote “YES”.

a) “A series of grants are requested to reach the end-goal…” - Could you possibly add a forecasted costing sheet to your roadmap so we understand how much we are talking about here? Surely your scope of work is massive, it’ll just be interesting to know for the sake of transparency.
b) I’m missing some comments about the go-to market strategy. What initiatives are you planning to fuel adoption of the platform and meet your ROI targets?

Thanks a lot in advance!

Hi guys! Thank you very much for the feedback and the compliment. I agree that a cost forecast adds a lot of value and transparancy, you can find it here:

We have multiple use-cases for Compute-to-SSI to fuel adoption and meet our ROI targets:

  • WorkPi has a filled pipeline with ~40 corporate enterprises (>1.000 employees each) that will use WorkPi for the measurement & development of their employees. Employees will be able to participate in assessments and enrich their data profile in order to be matched with jobs and development suggestions. WorkPi will use the Compute-To-SSI technology that we are currently developing in order to learn from this data. For the first ROI calculation we have decided to be modest and very realistic by only considering 50% of our launching client (3.000 employees). The ongoing pilot with this company will allow us to test the MVP of Compute-To-SSI already in December/January. We will go-to-market with a SaaS subscription of ~$5 per employee per month and the Compute-to-SSI functionality will be one of the most important components of this product. Because we expect that we will onboard 2-3 extra clients in the next few months, the short-term ROI will likely be sufficient to break even with the grants. However, our reason to build this technology goes way beyond the value that we personally derive from it:
    • WorkPi is not the only party that could derive value from the data that the users have generated. we allow our users to export the data to their SSI wallet and get rewarded for it by selling it on an Ocean Data Marketplace and we’re also exploring collaboration with other OceanDAO projects to leverage this data (DataUnion and newcomer Walt.ID for example). To see an example of how much career data is worth, I would like to refer to the ROI calculation in the proposal.
    • Besides career related data, many more types of data can be stored in SSI wallets, and shared/sold to train AI models. Since Compute-to-SSI will be open-source, other projects will be able to use the technology and it’s hard to predict what the ROI will be when they do.

For more information about WorkPi & Work X you can also have a look at this teaser pitch deck:

WorkPi Teaser Pitch Deck _compressed.pdf (1.4 MB)

I hope this answers your questions, if not please feel free to reach out. Also, it would be great to connect soon and see if there’s an opportunity to collaborate. I have some ideas that I would like to share.

Best regards,


1 Like

Hey Rik,

We just submitted our vote for this proposal and good luck.

Thanks also for reviewing our Proposal on the Port. Let us know whether you have any more comments.

Data Whale


Hi Data Whale, thank you very much for your vote and feedback! Good luck to you as well and to be honest we did not have any remarks on your proposal. It was really clear and we’re very enthousiastic about your progress, keep it up!