Project presentation. Ecompensa 2.0 (possible bounty or prize)

Misc
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Hello Zama Community Members,

Hope you are all doing great!

I am Bruno, an economist based in Lisbon. I have dedicated the last 15 years to research that naturally led me to the world of FHE, Zama, and several other inspiring startups in this field.

My work focuses on solving a critical challenge for real-world businesses: enabling them to safely share confidential financial data. By doing so, we can unlock an entirely new ecosystem of services that adds massive value and disrupts traditional methods.

I am reaching out to share my portfolio and, hopefully, spark some engaging conversations with you all. If you see a connection between my research and your own work in the FHE and PET space, I would love to learn more about your projects. Please feel free to share your website links here or send me a private message.

I also believe a dedicated hackathon could be a fantastic way to kick off collaboration on this front.

Looking forward to connecting!

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Hi Bruno,

Welcome to the community!

Just so you know: we run a Zama Developer Program, though the current season (Mainnet Season 3) wraps up today. There will be another season in the future, so keep an eye out. It could be a great way to get involved.

Best,
Jan

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Thanks, Jan. The objective is to enable 10,000 private clearing houses. The starting model is available in a notebook publicly shared.

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I made availabe a microsite fluidcluster.pt with the following sections: AI swarm (interaction with core) , Fusion (agent 10 merger or "Swap2 engine), Sandbox( the main algo), and Docs (documentaion

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I have selected some whitepaper discussions, maybe this will turn into some real applications. Not AI slop. Grounded research.

The Always-Encrypted Economy
The Macroeconomic and Computational Mechanics of Zero-Trust Data
Ecosystems https://fluidcluster.pt/assets/docs/encrypted_economy_white_paper.pdf

Hardware-Accelerated FHE
Netting Engine via Vectorized Zero-Sum Obfuscation and
Photonic Coprocessing

Vectorized Reformulation of the Primal Network Netting Optimization for Homomorphic and Co-Packaged Photonic Accelerators

Vectorized Linear Solver Pipelines:
Porting Pyomo Netting Models to FHE-Photonic Architectures

ECOMPENSA 2.0: Sovereign Post-Quantum Clearing Infrastructure
Technical Specifications of the Model 5 Set-Off Formulation,
FHE.select Branchless Invariant Constraints, and O(1) Photonic Co-Processing https://fluidcluster.pt/assets/docs/ecompensa-2.0-yellow-paper.pdf

ECOMPENSA 2.0: Sovereign
Post-Quantum
Clearing Infrastructure
Technical Specifications of the Model 5 Set-Off Formulation
and FHE.select Branchless Invariant Constraints https://fluidcluster.pt/assets/docs/ecompensa-whitepaper.pdf

Closing the Loop: Mathematical and Game-Theoretic
Foundations of Sovereign Multilateral Clearing and
Non-Monetary Trade Credit Networks

DEBT-TO-EQUITY SWAPS AND ABCP
RESTRUCTURING

Automated Confidential M&A Integration
Mechanism Design & Stable Coalition Incentives via fhEVM Platforms

Taming the Bullwhip Effect via FHE
Blind Demand Aggregation & Shapley Incentives on FluidCluster 2.0

Millions Of Businesses Solving Coordination Problems Privately Pitch

The Age of Confidential Collaboration
1 Million Apps for 1 Million Business Networks via FHE, MPC, and fhEVM

Sovereign Game Theory and Confidential Restructuring: Implementing
Shapley Cost Allocations and Blind M&A Debt-Equity Swaps on the
Zama FHEVM

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Today I finished a demo that basically engineers this setup as a public blockchain. We still need to hide the topology of the trades and I use the KMS model of Zama. We would prefer to rotate this key for each netting round. The systems is dynamic which means it may not settle if parties don’t want. We could run model CCP (M1) entirely in FHE but then we would need collateral. This can be deployed as pooled invoices against pooled stable coin funds.

We changed the logic so the requests (from the creditor) and confirmations (of the debtor) are done before the netting processing engine, so there are no “two step” rounds within the engine. All is hash-chained including user and processor (ZK) mathematical proofs of balance conservation and limit / collateral constraints are respected

scalable netting (M4 wihtou novation) toy model

triparty set-off toy model (M5)

ABCP vs CBDC or stable coins (eg. using debt, not our preference)

confidential stable coins . our take aways:

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A demonstration of the efficiency of novation.
We identified 5 distinct models of debt compensation
Our algorithm encapsulates all models and variants.

In the paper below we compare M5 to M4 (model 5 set-off vs model 4 with novation with credit constraints)

M1 is the easiest one to grasp. (the tradicionall CCP central counterparty ). Any junior developer can code it with FHE. But will he understand the risk and collateral trade-offs associated with each model?

Multilateral Set-Off Netting with Novation
Constraints
and Discretionary Credit Caps

The Demo works as expected, By opening the specific channel we reduce the volume or external tokens (cash, swap, you name it) needed to clear all debts:

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We finish our public presentations as we move on to a private AI Lab now. Expecting to start coding, testing and production in 2027. Thanks for your attention. There are tons of oher privacy Apps (in the optimization field and other utilities) to leverage the data space we intend to setup.

Privacy-Preserving Multilateral Netting via
Vectorized W-Space Optimization with
Multi-Party Computation Topology Obfuscation:

Paradigm Shift in Liquidity Clearing: From Digital
Pyomo Enclaves to Analogue Photonic
Multilateral Netting Solvers:

Generalizing Linear Programming
Minimum-Cost Flow Problems for Homomorphic
Silicon-Photonic Solvers