Confidential
& Private Computing
Assess and integrate trusted execution environments, zero-knowledge proofs, and computation over encrypted data. Start with the privacy goal, a defined threat model, and measurable acceptance criteria.
The work
Define what stays private and what must be verified.
TEE engineering and attestation
Define a hardware-protected processing boundary, attestation policy, and controlled secret release.
Zero-knowledge proofs and verification
Prototype private proofs and verifier integration with explicit statements and trust assumptions.
FHE feasibility and encrypted computation
Benchmark selected encrypted calculations for correctness, precision, latency, memory, and cost.
Research with measured limits
Identify the sensitive inputs, who may see the result, and which operator or system is outside the trust boundary. A feasibility study compares suitable methods on representative tasks before a wider build.
The founder’s résumé records TEE and secure-storage research at Kin and zero-knowledge proof research at Hypercent. ZK and FHE engagements start with a defined feasibility or integration scope. Production protection and performance require evidence from the selected workload and environment.
Private computing methods
TEE engineering and attestation
Evaluate a trusted execution environment for sensitive processing, including AI workflows. Define the trusted hardware and software, verify attestation evidence, and control secret release. Plaintext exists inside the protected boundary; external calls, outputs, logs, and recovery also need review.
Zero-knowledge proofs and verification
Define the statement, private inputs, public outputs, and source of trusted input data. Build a bounded proof and verifier integration with an established proof system, including zk-SNARKs where suitable. Test invalid claims, setup assumptions, proof time, and verification cost.
FHE feasibility and encrypted computation
Prototype selected calculations over encrypted data with an established FHE library. Record key ownership, allowed decryption, precision, memory, latency, and cost against a plaintext baseline. Suitability is decided from the workload results; FHE does not by itself establish correct execution or prevent disclosure through authorized outputs.
Illustrative workflow
Confidential AI & document processing
- 01
Map the disclosure
Identify document inputs, permitted outputs, and external calls.
- 02
Verify the environment
Check attestation against the agreed policy before releasing secrets.
- 03
Process within the boundary
Run the selected model and inspect logs, storage, and output paths.
- 04
Measure and decide
Compare quality, latency, cost, and failed-policy behavior before expansion.
Illustrative TEE workflow. It is not a claim of deployed confidential AI. Hardware, code, external services, output disclosure, and operating ownership must be assessed together.
Read the exampleWhat you receive
A tested boundary and a measured next step.
A data-flow and threat model with protected information, trust assumptions, and permitted disclosure.
A method comparison and a bounded prototype or integration using established components.
Recorded correctness checks, negative tests, and security assumptions for the agreed boundary.
Workload measurements for latency, memory, proof or verification cost, and precision where relevant.
Key, attestation, output, and operating requirements, with known limitations and next steps.
Before we start
Do TEE, ZK, and FHE provide the same protection?
They address different requirements. A TEE isolates execution with hardware trust. ZK proves a statement while hiding specified inputs. FHE computes over encrypted inputs. We select and test the method against the threat model and permitted outputs.
Can this work without a blockchain?
Yes. Confidential processing and private verification can be integrated into conventional applications and data platforms. Blockchain integration is included only when the workflow requires it.
Can you promise FHE performance before a study?
Performance depends on the calculation, scheme, parameters, data size, and hardware. A feasibility study records a plaintext baseline and encrypted measurements, then recommends whether to continue.
Contact
Discuss a privacy requirement
Tell us your goal, context, and constraints. For a focused technical consultation, we agree the question, preparation, session scope, and written next steps before work begins.
Discuss your project