Privacy in institutional finance is not simply about hiding transaction details. The more important questions are: Who can see the data? What remains visible to network participants? And who has the authority to access confidential information?
I looked into two different approaches to these questions: Rayls Enygma and Partisia MPC.
Although both work with private data, they address different problems.
Rayls Enygma: Confidential Institutional Transfers
Rayls Enygma is designed to support confidential transfers between institutions operating on separate Sovereign chains.
Its privacy model combines cryptographic commitments and zero-knowledge proofs to verify transactions without exposing sensitive transfer details to ordinary Hub observers.
However, privacy does not mean that every participant becomes invisible.
The sender remains identifiable on the Hub, while the receiver is obscured within an anonymity set of up to six participants. This protection depends on network size and does not guarantee meaningful anonymity in a very small network.
There is also an important distinction between ordinary observers and the network operator.
The operator's governance services can decrypt cross-chain transaction information using the participants' encrypted view keys. This provides an audit and monitoring capability, but it also means that operator access is part of the privacy model.
Looking Beyond the Documentation
One important part of the investigation was examining the Enygma repository rather than relying only on high-level privacy descriptions.
The enygma_payments reference implementation contains a Fix M-12 notice stating that its Section 7 auditing mechanisms are design-only and not implemented in that reference implementation.
This finding needs careful interpretation. It does not mean that auditing is absent from every integrated Rayls Private Network deployment. The integrated architecture documents separate governance services and auditing capabilities.
Another important consideration is the Groth16 trusted setup. Its security depends on the setup process, and the development parameters in the public repository should not be confused with production ceremony parameters.
Partisia MPC: Confidential Computation
Partisia takes a different approach.
Instead of focusing specifically on institutional asset transfers, its Multi-Party Computation framework allows multiple parties to compute over private inputs without revealing those inputs to one another.
Inputs are distributed as secret shares among MPC nodes. The contract determines how computation is initiated and how results are disclosed.
The Vickrey auction example provides a useful code-level illustration.
Its auction_compute_complete callback uses ZkStateChange::OpenVariables to open selected output variables. The following callback reads those variables and constructs the auction result.
This demonstrates an important principle: keeping inputs private does not necessarily mean keeping every computation result private. Output disclosure can be controlled through contract logic.
Two Different Privacy Models
The comparison reveals an important distinction.
Rayls Enygma focuses on confidential institutional transfers, with a defined operator governance and auditing model.
Partisia MPC focuses on confidential computation over private inputs, with output disclosure determined by the application contract.
They are not interchangeable systems, and their privacy guarantees should not be treated as equivalent. Partisia's general computation framework also does not automatically provide cross chain institutional settlement.
Final Thoughts
The most interesting finding is that privacy is not a single feature.
A system can hide transaction amounts from ordinary observers while allowing an authorised operator to inspect cross chain activity. Another system can keep inputs secret during computation while deliberately revealing selected results.
Understanding these boundaries is more useful than simply calling a platform private.
For institutional finance, the real question is not only what a system hides, but also what it reveals, to whom, and under which conditions.