Grant Proposal - Homomorphic Encryption for Ocean Market

OceanDAO Round 9
#1

Grant Proposal - Homomorphic Encryption for Ocean Market

Part 1 - Proposal Submission (*Mandatory)

Name of Project:

Homomorphic Encryption for Ocean Market

Proposal in one sentence:

Homomorphic Encryption based solution to preserve intellectual property and/or privacy of monetized data for participant X and AT THE SAME TIME preserve intellectual property and/or privacy of the monetized algorithm/model applied on that data for participant Y for applications in healthcare and finance.

Description of the project and what problem is it solving:

The Ocean Market currently supports

  • monetization of data OR

  • monetization of algorithms.

Data can be monetized by publishing a dataset. The seller loses intellectual property and/or privacy to the buyer by sharing his data.

Algorithms can be monetized with “Compute to Data”. The seller loses intellectual property and/or privacy by sharing his algorithm/model.

In both cases the buyer is protected.

Protecting BOTH parties, seller and buyer, AT THE SAME TIME (more precise for the same interaction) is currently not possible in a convenient manner, not even for the simplest algorithms/models.

This can lead to a blocking-scenario: If at least one party considers the potential damage bigger than the upside of interaction the interaction will not take place.

Homomorphic Encryption for Ocean Market solves this problem for a selected use-case to demonstrate it to a broader audience. It further creates an open-source-blueprint for applications in healthcare and finance.

Grant Deliverables: (Provide us with a check-boxed list of deliverables for the funding provided.)

  • [X] Detailed specification of the use-case in healthcare or finance

  • [X] “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market

  • [X] “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market

  • [X] “App” provided as open-source solution which integrates and makes previous deliverables accessible via https://ocean-he.tributech.io for an E2E-demonstration of Homomorphic Encryption on Ocean Market

Which category best describes your project? Pick one.

  • [X] Build / improve applications or integrations to Ocean

  • [ ] Outreach / community / spread awareness (grants don’t need to be technical in nature)

  • [ ] Unleash data

  • [ ] Build / improve core Ocean software

  • [ ] Improvements to OceanDAO

Which Fundamental Metric best describes your project? Pick one.

  • [X] Data Consume Volume

  • [ ] Datatoken Contracts

  • [ ] Total Value Locked

  • [ ] Network Revenue

  • [ ] Market WAU (Weekly market participants in Ocean Market or across all data markets)

  • [ ] $ Proposed / $ Funded (Your project helps generate, and fund more proposals)

  • [ ] Other

What is the final product?:

The final product will be an E2E-demonstration of a use-case for healthcare or finance which can be accessed by a web-portal which enables

  • monetizing data of participant X AND

  • monetizing an algorithm/model provided by participant Y

BOTH AT THE SAME TIME preserving privacy and protecting intellectual property for participants X and Y on the Ocean Market.

In addition to the working E2E-demonstration it will be an open-source blueprint for additional applications.

The ROI will be generated by every use-case which requires BOTH guarantees outlined above at the same time.

According to the market report “GLOBAL HOMOMORPHIC ENCRYPTION MARKET REPORT, HISTORY AND FORECAST 2016-2027, BREAKDOWN DATA BY COMPANIES, KEY REGIONS, TYPES AND APPLICATION" the market was valued at US$ 120.12 million in 2019 and is projected to reach US$ 246.29 million by 2027; it is expected to grow at a CAGR of 9.7% from 2020 to 2027.

Press release to the cited report can be found here.

The assumptions are the following:

  • The considered duration for the ROI will be 1 year (calculation based on 2022)

  • The solution will bring 0.3% of EXISTING- and 1% of NEW/EXTENDED homomorphic encryption use cases to the Ocean Market

  • Shareholders of Homomorphic Encryption use cases on Ocean Market accept to pay 20% to sell their data/algorithms/models on the Ocean Ecosystem.

  • The chance of success is 70%


bang 
    =   (
            (
                (Homomorphic Encryption Market share on Ocean Market for EXISTING use-cases) +
                (Homomorphic Encryption Market share on Ocean Market for NEW/EXTENDED use-cases)
            ) 
            * (% for revenues to the OCEAN Market ecosystem based on Market share of Ocean Market for HE use-cases)
        )
    =  (
            ($144,553,489.08 * 0.003) +
            ($14,021,688.44 * 0.01)
        )
        * 0.2
    =   $114,775.47 

buck  
    = $17,500

(% chance of success) 
    = 70%

ROI 
    = (bang / buck) * (% chance of success) 
    = ($114,775.47 / $17,500) * 0.7
    = ~4.59

ROI = ~4.59

Details can be found at ROI.pdf (424.6 KB)

Funding Requested:

$17,500

Proposal Wallet Address:

0x69E49561bbdB44CaF87ae6505f7330bAf8dD32BF

(must have minimum 500 OCEAN in wallet to be eligible. This wallet is where you will receive the grant amount if selected).

Have you previously received an OceanDAO Grant (Y/N)?

No.

Team Website (if applicable):

https://tributech.io

Project lead Contact Email:

s.pfeifhofer@tributech.io

Country of Residence:

Austria

Part 2 - Proposal Details (*Recommended)

Project Deliverables - Category:

If the project includes software:

From a conceptual perspective Homomorphic Encryption for Ocean Market will work as sketched in the following diagram.

The crucial property of the proposal is that algorithms/models can be executed with encrypted data as input and deliver encrypted insights.

On one hand side the raw-data and the insights will not be available in plain-text to the “Insights generator”. On the other hand the algorithm/model will never be exposed to the “Data provider & Insights consumer”.

Both parties can collaborate and benefit from each other without sharing intellectual property or privacy.

In general it might be necessary for certain use-cases to split the two parties “Data provider & Insights consumer”. Deliverable 1 (see below) will outline if it’s necessary for the use-case.

Architecture
Architecture918Ă—632 78 KB

The parts “Encryption module” and “Inference module” in the diagram above and mentioned below as deliverables 2 und 3 will be implemented in C, C++ and C# (dotnet). Source-code and the documentation will be available on GitHub with appropriate CI-pipelines to build public docker-images.

The deliverable 4 mentioned below will be implemented as web-app using Angular with MetaMask-integration.

Project Deliverables - Roadmap

  • Any prior work completed thus far?

    Tributech is one of the project partners of the research project “Secure Machine Learning Applications with Homomorphically Encrypted Data” (alias SMiLe). More details can be found here. The ongoing project has already shown the current state of research, what can be realized with homomorphic encrypted data and where the limitations/overheads are. Furthermore a technology research on available frameworks (e.g. Fully Homomorphic Encryption (by Google) or Microsoft SEAL) has been conducted.

  • What is the project roadmap? That is: what are key milestones, and the target date for each milestone.

    1. Detailed specification of the use-case. Deliverables: Specification (available on GitHub). Date: Q3 2021.

    2. “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021.

    3. “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021 - Q1 2022.

    4. “App” provided as open-source solution which integrates and makes 2. and 3. accessible via https://ocean-he.tributech.io for an E2E-demonstration of Homomorphic Encryption on Ocean Market. Deliverables: Documentation, source code, app (available on GitHub and a public docker registry; hosted by Tributech and reachable at the mentioned URL). Date: Q1 2022 - Q2 2022.

Team members

Simon Pfeifhofer

Michael Bernhofer

Christian Lumper

Maximilian Mayr

4 Likes
OceanDAO Round 9
Homomorphic Encryption for Ocean Market | Homomorphic Encryption demonstrator | Round 18
#2

Hello from ResilientML

This looks like an interesting value proposition and well-structured team for Ocean and should you be successful this would be a valuable tool for the types of FaaS marketplaces and compute-to-data solutions we are developing in ResilientML on Ocean marketplace.

I hope you won’t mind that I have several questions after reading your interesting proposal. I have some questions that I would seek your insight on regarding the deliverables:

Q1. One challenge that has been present in homomorphic encryption solutions relates to the efficiency of these methods from a computational perspective - how do you see this challenge being addressed or is this a challenge in your solution? If not please elaborate or provide a reference to us for further reading - it would be interesting.

Q2. What type of homomorphism are you exploring:
multiplicative like the well-known RSA?
additive like the Pallier algorithm?
Brakerski-Gentry-Vaikuntanathan (BGV) solution?
Brakerski-Fan-Vercauteren (BFV) solution?
Craig-Gentry type solution that combines these two…
or other non-linear?
– note this would be of interest to us as it would influence the types of ML-algos we may be able to utilize in this solution once you have developed it.

Q3: I would also be interested to know if you plan on having a multi-sample or federated learning component to this solution as you develop it further?

Q4: What advantages does your solution have over existing open-source libraries for homomorphic encryption solutions such as PALISADE?

best wishes and look forward to your thoughts - ResilientML team.

2 Likes
#3

I like what you a building here and the secure computation environment would be a game changer. I guess I am going to support this proposal and I am crossing fingers for the round.

1 Like
#4

@Gareth:

Q1
Thank you for asking the question to give us the opportunity to address this important aspect and provide context for other readers.
There is (still) a significant computational overhead for (fully) homomorphic encryption. The factor of computational overhead depends on the used scheme of homomorphic encryption.
Prominent schemes of homomorphic encryption are:

  • CKKS
  • BFV
  • BGV
  • TFHE

Different encryption schemes support different classes of computations over encrypted data. Classes of homomorphic encryption are:

  • Partially homomorphic encryption
  • Somewhat homomorphic encryption
  • Leveled homomorphic encryption (LHE)
  • Fully homomorphic encryption (FHE)

The computational overhead increases in that order. We will probably use Microsoft SEAL (class LHE) or Fully Homomorphic Encryption (by Google) (class FHE) for the implementation of deliverable 2 and 3.
Therefore HE is not suited for every confidential computation use case. HE is suited well for use cases where the value of privacy and intellectual property is bigger than the computational overhead.
Our aim is to provide evidence why the use case for the demonstration in finance or healthcare (see deliverable 1) will fall into that category.

Q2
Please see answer to Q1.

Q3
As already anticipated it would exceed the scope of the proposal. It could be a future extension.

Q4
Please see answer to Q1.

1 Like
#5

@simon.pfeifhofer Round 9 Max is $17,500, Round 8 Max was $17,600. I changed to $17,500 in our DAO database, please update it in your proposal.

1 Like
#6

@AlexN: Proposal updated.

1 Like
#7

We would like to give an update on the Grant Proposal Homomorphic Encryption for Ocean Market.

[Deliverable Checklist]

  • [X] Detailed specification of the use-case. Deliverables: Specification (available on GitHub). Date: Q3 2021.

  • [ ] “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021.

  • [ ] “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021 - Q1 2022.

  • [ ] “App” provided as open-source solution which integrates and makes 2. and 3. accessible via https://ocean-he.tributech.io 7 for an E2E-demonstration of Homomorphic Encryption on Ocean Market. Deliverables: Documentation, source code, app (available on GitHub and a public docker registry; hosted by Tributech and reachable at the mentioned URL). Date: Q1 2022 - Q2 2022.

More details on the completed deliverables:

Tributech contacted a list of companies which are potential providers/consumers of HE-services. In context of the research-project SMiLe interviews with various companies have been done.

The most promising category of use-cases which we identified is topic modelling in the context of Natural Language Processing. It is described as a method of uncovering hidden structure in a collection of texts.

The use-case which gathered most attention and is our current candidate for further investigation is the use case described in detail on GitHub.

We are sorry for being behind of schedule. To find a use-case was more time-consuming than expected. The major reason was the adoption of (F)HE as a technology with its associated obstacles.

@AlexN

FYI: The update was posted by @simon_pfeifhofer and not the “original” account @simon.pfeifhofer. It’s still me. At the time of writing there was an issue on port with the “original” account and it was not possible to access it and write the post. I’m sorry for that.

#8

Hi @simon_pfeifhofer, thanks for the update. I have registered it into Airtable.

1 Like
#9

We would like to give an update on the Grant Proposal Homomorphic Encryption for Ocean Market.

[Deliverable Checklist]

  • [X] Detailed specification of the use-case. Deliverables: Specification (available on GitHub). Date: Q3 2021.

  • [ ] “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021.

  • [ ] “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021 - Q1 2022.

  • [ ] “App” provided as open-source solution which integrates and makes 2. and 3. accessible via https://ocean-he.tributech.io 7 for an E2E-demonstration of Homomorphic Encryption on Ocean Market. Deliverables: Documentation, source code, app (available on GitHub and a public docker registry; hosted by Tributech and reachable at the mentioned URL). Date: Q1 2022 - Q2 2022.

More details on the completed deliverables:

The completed deliverable is the detailed description of the use-case which we aim to implement as a blue-print for future-application using homomorphic encryption on the Ocean Market.

Tributech contacted a list of companies which are potential providers/consumers of HE-services. In context of the research-project SMiLe interviews with various companies have been done.

The most promising category of use-cases which we identified is topic modelling in the context of Natural Language Processing. It is described as a method of uncovering hidden structure in a collection of texts.

The use-case which gathered most attention and is our current candidate for further investigation is the use-case described in detail on GitHub.

We are sorry for being behind of schedule. To find a use-case was more time-consuming than expected. The major reason was the adoption of (F)HE as a technology with its associated obstacles.

#10

We would like to give an update on the Grant Proposal Homomorphic Encryption for Ocean Market.

[Deliverable Checklist]

  • [X] Detailed specification of the use-case. Deliverables: Specification (available on GitHub). Date: Q3 2021.
  • [P] “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021.
  • [P] “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Date: Q4 2021 - Q1 2022.
  • [ ] “App” provided as open-source solution which integrates and makes 2. and 3. accessible via https://ocean-he.tributech.io 7 for an E2E-demonstration of Homomorphic Encryption on Ocean Market. Deliverables: Documentation, source code, app (available on GitHub and a public docker registry; hosted by Tributech and reachable at the mentioned URL). Date: Q1 2022 - Q2 2022.

More details on the deliverables:

The detailed description of the use-case which we aim to implement as a blue-print for future-application using homomorphic encryption on the Ocean Market has been completed last time, no change since then.

For the deliverables “Inference module” and “Encryption module” marked with “P” there are three repositories where implementation happens:

The first repository contains a sample how the library concrete can be used for a FHE-computation. The latter two repositories use concrete and tenseal and contain the implementation which gets done inside the research-project SMiLe. They are still private but at least one of them will be available open source with a permissive license.

A paper has been submitted for the conference FHE.org.

HE-MAN – Homomorphically Encrypted MAchine learning with oNnx models
Abstract: Machine learning (ML) algorithms are increasingly important for the success of
products and services considering the growing amount and availability of data. This also holds
for areas handling sensitive data, e.g. medical applications. However, people are reluctant
to send their personal sensitive data to a ML service provider. Homomorphic encryption
(HE) is a promising technique to enable people using ML services without giving up privacy.
Despite steady improvements, HE is still hardly integrated in today’s ML applications. We
introduce HE-MAN, a two-party machine learning toolset for privacy preserving inference
with ONNX models and homomorphically encrypted data. Both, the model and the input
data do not have to be disclosed. Furthermore, expertise in HE is not required as HE-MAN
performs homomorphic inference while abstracting cryptographic details away from the user.
We evaluate our tools on two different neural network image classifiers, namely hand-written
digits and cancer tumors.

The mentioned machine learning toolset gets implemented in the two repositories. In a first step the target is to support plaintext-models in ONNX-format and elementwise additions and multiplications,
matrix multiplications, convolutions, average pooling and batch norms as operations applicable to homomorphic encrypted input.

We apologise of beeing behind of schedule but at the same time happy to share the very promising progress.

#11

Hi @simon.pfeifhofer @simon_pfeifhofer, you’ll need to submit your deliverables via the proposal portal for R16.

Please read the instructions here and do it ASAP so we can get you accepted + registered into R16.

If you need any further help, please follow the instructions in the message above.
I have also DM’d you on Discord.

All the best!

  • Idiom
1 Like
#12

Project submitted deliverables:

[x] Detailed specification of the use-case. Deliverable has been completed and the results are available on GitHub.

[x] “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Please see detailed explanations below.

[x] “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market. Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry). Please see detailed explanations below

[x] “App” provided as open-source solution which integrates and makes 2. and 3. accessible via https://ocean-he.tributech.io 7 for an E2E-demonstration of Homomorphic Encryption on Ocean Market. Deliverables: Documentation, source code, app (available on GitHub and a public docker registry; hosted by Tributech and reachable at the mentioned URL). Please see detailed explanations below.

For the deliverables “Inference module” and “Encryption module” there are three repositories where implementation happens:

The first repository contains a sample how the library concrete can be used for a FHE-computation. The latter two repositories use concrete and tenseal and contain the implementation which gets done inside the research-project SMiLe. They are still private but at least one of them will be available open source with a permissive license.

A paper has been submitted for the conference FHE.org.

HE-MAN – Homomorphically Encrypted MAchine learning with oNnx models

Abstract: Machine learning (ML) algorithms are increasingly important for the success of

products and services considering the growing amount and availability of data. This also holds

for areas handling sensitive data, e.g. medical applications. However, people are reluctant

to send their personal sensitive data to a ML service provider. Homomorphic encryption

(HE) is a promising technique to enable people using ML services without giving up privacy.

Despite steady improvements, HE is still hardly integrated in today’s ML applications. We

introduce HE-MAN, a two-party machine learning toolset for privacy preserving inference

with ONNX models and homomorphically encrypted data. Both, the model and the input

data do not have to be disclosed. Furthermore, expertise in HE is not required as HE-MAN

performs homomorphic inference while abstracting cryptographic details away from the user.

We evaluate our tools on two different neural network image classifiers, namely hand-written

digits and cancer tumors.

The mentioned machine learning toolset gets implemented in the two repositories. In a first step the target is to support plaintext-models in ONNX-format and elementwise additions and multiplications, matrix multiplications, convolutions, average pooling and batch norms as operations applicable to homomorphic encrypted input.

The deliverables “Encryption module”, “Inference module” and “App” are still ongoing. The good news are that there were no showstoppers and we can proceed as planned. The bad news is, that we drastically underestimated the required effort because of the fact that everything the we can use to build on top in the (F)HE-space is still very basic. There is a steep learning curve and a communication overhead to get everyone on the same page. We would like to apply again in order to be able to continue our work, apologise for the delay and hope that our reasons why we where not able to ship all promised deliverables are understandable.

1 Like
#13

Admin: Hi Simon, thanks a lot for submitting your deliverables.

I was going through them and had a hard time visiting all repos you shared, some were down/404, like:
https://ocean-he.tributech.io/
https://github.com/tributech-solutions/fhe-concrete
https://github.com/smile-ffg/concrete-inference
https://github.com/smile-ffg/tenseal-inference.

BUT: It’s obvious that there are deliverables worked on and completed (also wrt your documentation earlier here).

Maybe they are still “behind your product wall”?

Maybe just update the links to the repos.

We loved that you won a Tender from FFG, documented right here: SMiLe

Congrats on this!

Update some of your repos/demos so the DAO can see more of your hands-on work, and we’ll be more than happy to accept your deliverables.

Again: Congrats on the tender and looking forward to you resubmitting the missing/404 links and greenlight you for your next proposal asap!

Thx

1 Like
#14

@oceandao thank you for your feedback.

Links:

https://ocean-he.tributech.io/:
It’s the link to the final app which integrates everything. It’s part of the last deliverable “App” which we didn’t manage to finish. Therefor it’s not reachable. Please read the previous post.

https://github.com/tributech-solutions/fhe-concrete:
It’s public now. Sorry we forgot to change it. My bad.

https://github.com/smile-ffg/concrete-inference and
https://github.com/smile-ffg/tenseal-inference:
Please read the previous post. The partners of the research-project don’t want to make them public until there is a solid version which supports the promised functionalities. Unfortunately we cannot force it without the agreement of all research-institutions which are involved. The current target is to support a demo application based on the MNIST database.
I mentioned

They are still private but at least one of them will be available open source with a permissive license.

Currently it seems like that both of them will be further developed (and made public) because the two frameworks which are in use have different strengths and weaknesses.

#15

Hi Simon, can you please re-submit your deliverables w/ these updates so we can sign off on them?

1 Like
#16

Project submitted deliverables:

[x] Detailed specification of the use-case. Deliverables: Specification (available on GitHub).

[x] “Encryption module” provided as open-source software for homomorphic encryption of datasets for the Ocean Market.

Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry).

[x] “Inference module” provided as open-source software for computation/inference based on Homomorphic Encrypted datasets of the Ocean Market.

Deliverables: Documentation, source code, docker image (available on GitHub and a public docker registry).

[x] “App” provided as open-source solution which integrates and makes 2. and 3. accessible via https://ocean-he.tributech.io for an E2E-demonstration of Homomorphic Encryption on Ocean Market.

Deliverables: Documentation, source code, app (available on GitHub and a public docker registry; hosted by Tributech and reachable at the mentioned URL).

Details/Update:

Deliverable detailed specification

The detailed description of the use-case which we aim to implement as a blue-print for future-application using homomorphic encryption on the Ocean Market has been completed last time, no change since then. Details can be found here: https://github.com/tributech-solutions/ocean-he.

Deliverables “Encryption module” and “Inference module”

For the deliverables “Inference module” and “Encryption module” marked with “x” there are three repositories where implementation happens:

Tributech FHE sample repository using concrete: https://github.com/tributech-solutions/fhe-concrete

SMile research project repository using concrete: https://github.com/smile-ffg/concrete-inference

SMile research project repository using tenseal: https://github.com/smile-ffg/tenseal-inference

The first repository contains a sample how the library concrete can be used for a FHE-computation. The latter two repositories use concrete and tenseal and contain the implementation which gets done inside the research-project SMiLe. They are still private but they will be available open source with MIT license when all research-partners are confident that the repositories represent a solid base which can fruitfully evolve.

A paper has been submitted for the conference FHE.org.

HE-MAN – Homomorphically Encrypted MAchine learning with oNnx models

Abstract: Machine learning (ML) algorithms are increasingly important for the success of

products and services considering the growing amount and availability of data. This also holds

for areas handling sensitive data, e.g. medical applications. However, people are reluctant

to send their personal sensitive data to a ML service provider. Homomorphic encryption

(HE) is a promising technique to enable people using ML services without giving up privacy.

Despite steady improvements, HE is still hardly integrated in today’s ML applications. We

introduce HE-MAN, a two-party machine learning toolset for privacy preserving inference

with ONNX models and homomorphically encrypted data. Both, the model and the input

data do not have to be disclosed. Furthermore, expertise in HE is not required as HE-MAN

performs homomorphic inference while abstracting cryptographic details away from the user.

We evaluate our tools on two different neural network image classifiers, namely hand-written

digits and cancer tumors.

The mentioned machine learning toolset gets implemented in the two repositories. In a first step the target is to support plaintext-models in ONNX-format and elementwise additions and multiplications, matrix multiplications, convolutions, average pooling and batch norms as operations applicable to homomorphic encrypted input.

Deliverable "App"

The deliverable wires everything together and makes it accessible for OCEAN. Since the deliverables above are still in progress it was not possible to start with it.

We apologise of beeing behind of schedule but at the same time happy to share the very promising progress. We drastically undererstimated the effort and it was necessary to invest way more resources than expected to reach the current state. Applying homomorphic encryption to real world use-cases is still a very demanding topic. The holy grail of cryptography is not something easy … . We would be very happy if it would be possible to complete the proposal and continue our journey with a new proposal.

1 Like
#17

Admin:

Hi @simon.pfeifhofer,

thank you for submitting another update for your previous proposal! Your Grant Deliverables have been reviewed and look to be in good condition.

I have also looked at your Project Standing, it looks to be in good condition and ready to apply for another grant.

Thank you especially for detailing on the “app” part, as in:

"Deliverable “App”

„The deliverable wires everything together and makes it accessible for OCEAN. Since the deliverables above are still in progress it was not possible to start with it.

We apologise of being behind schedule but at the same time happy to share the very promising progress.

We drastically underestimated the effort and it was necessary to invest way more resources than expected to reach the current state. Applying homomorphic encryption to real world use-cases is still a very demanding topic.

The holy grail of cryptography is not something easy … . We would be very happy if it would be possible to complete the proposal and continue our journey with a new proposal.“

This is fine and we cherish your ongoing effort to crack this „holy grail of cryptography“ for the Ocean Protocol Ecosystem.

Pls share with the OceanDAO fam when the app and the rest of the repos are online, maybe demo it in a grantee update or deep share in town hall or do a public comms measure / tweet / demo that we can amplify when you are there!

I would like to thank you for your positive contributions to the Ocean Ecosystem and I look forward to reviewing future proposals from your project.

All the best!
Your OceanDAO Team

1 Like