Jointly train or infer on combined datasets using hardware-secured enclaves, keeping all private data confidential.
Services
Secure Multi-Party AI Computation Services
Engineer confidential computing systems that enable multiple organizations to jointly train or infer on combined datasets without exposing their private data to each other, using hardware-based Trusted Execution Environments (TEEs) for secure aggregation and computation.
SECURE MULTI-PARTY COMPUTATION
Collaborate on AI Without Sharing Your Data
Enable strategic partnerships and consortiums without data exposure. Our secure multi-party AI computation services use hardware-based Trusted Execution Environments (TEEs) like Intel SGX and AMD SEV to create a neutral, verifiable computation space. > All parties contribute data, but no single party—including the infrastructure provider—can access the raw inputs.
- Secure Aggregation: Perform federated averaging, gradient updates, or inference on pooled data within an attested enclave.
- Provable Confidentiality: Cryptographic attestation verifies the integrity of the secure environment before any data is processed.
- Regulatory Alignment: Designed for compliance with GDPR, HIPAA, and the EU AI Act where data-in-use protection is mandated.
This approach solves critical collaboration barriers in sectors like multi-hospital clinical trials, cross-bank fraud detection networks, and supply chain optimization where data sensitivity prevents traditional data pooling. It transforms proprietary data from a liability into a secure, shared asset.
For related architectures, explore our services on Federated Learning Systems Engineering and Confidential AI Inference Enclave Development. To protect models themselves, see Encrypted AI Model Deployment and Management.
TANGIBLE RESULTS
Business Outcomes of Secure Multi-Party AI
Our engineering delivers secure, collaborative AI systems that unlock new data partnerships while eliminating the risk of exposing proprietary information. Move from theoretical possibility to production-ready, compliant solutions.
01
Joint Model Training Without Data Sharing
Enable multiple organizations—such as competing hospitals or financial institutions—to collaboratively train a superior AI model on their combined datasets. Sensitive raw data never leaves its owner's secure enclave; only encrypted model updates are shared for secure aggregation. This breaks down data silos for innovation while maintaining strict confidentiality.
Zero Exposure
Of Raw Training Data
Collaborative
Cross-Entity Innovation
02
Regulatory Compliance by Design
Architect systems that inherently satisfy data-in-use protection requirements under GDPR, HIPAA, and the EU AI Act for multi-party scenarios. By processing data within hardware-based Trusted Execution Environments (TEEs), you demonstrate technical due diligence and mitigate regulatory risk for cross-border AI initiatives.
Inherent
GDPR/HIPAA Compliance
Audit-Ready
Attestation Logs
03
Protection of Core Intellectual Property
Safeguard proprietary algorithms, model weights, and unique training methodologies when performing inference on another party's data. The computation occurs within your attested enclave, ensuring your IP is never exposed to the data provider or the underlying infrastructure, enabling new commercial AI-as-a-Service models.
IP Isolation
In Secure Enclaves
New Revenue
AI Service Models
04
Federated Learning at Scale with Integrity
Deploy production-grade federated learning systems where parameter exchange is cryptographically verified and occurs within TEEs. This prevents model poisoning attacks and ensures the integrity of the global model, making decentralized learning viable for sensitive sectors like finance and healthcare. Learn about related federated learning engineering.
Verified
Update Integrity
Resistant
To Poisoning Attacks
05
Enable High-Value Data Partnerships
Create the technical foundation for previously impossible collaborations. For example, combine retailer transaction data with credit card spending patterns for a holistic fraud detection network, or link pharmaceutical R&D data across biotech partners for accelerated discovery—all without any party seeing the other's raw data.
New
Collaborative Models
Unlocks
Latent Data Value
06
Cryptographic Audit Trails & Attestation
Generate irrefutable, hardware-rooted proof that computations executed exactly as specified within verified enclaves. This provides non-repudiable audit trails for compliance, builds trust between adversarial parties, and is essential for use in regulated industries and smart contract execution. Explore our work on confidential AI for financial modeling.
Hardware-Rooted
Proof of Execution
Essential for
Adversarial Trust
Secure Multi-Party AI Computation
Typical Project Timeline & Deliverables
A structured roadmap for engineering a confidential computing system where multiple parties can jointly compute on combined datasets without exposing private data.
| Phase & Deliverables | Starter (Proof-of-Concept) | Professional (Production-Ready) | Enterprise (Multi-Organization) |
|---|---|---|---|
Project Duration | 6-8 weeks | 10-16 weeks | 20+ weeks (custom) |
Core Architecture Design | |||
TEE Environment Setup (e.g., Intel SGX, AMD SEV) | Single cloud provider | Multi-cloud or hybrid | Cross-cloud with attestation orchestration |
Secure Multi-Party Computation Protocol Implementation | Basic secure aggregation | Advanced MPC with malicious security | Custom protocol with formal verification |
Integration with Existing Data Pipelines | 1-2 data sources | 3-5 federated data sources |
|
Attestation & Key Management Service | Basic remote attestation | Automated, policy-driven attestation | Centralized governance for multiple organizations |
Performance Benchmarking & Optimization | Latency & throughput baseline | Optimized for production scale | Continuous optimization SLA |
Security Audit & Penetration Testing | Internal review | Third-party audit report | Continuous red teaming program |
Deployment & Orchestration | Manual deployment scripts | Kubernetes operator for TEEs | Enterprise-grade orchestration platform |
Ongoing Support & Maintenance | Email support | 24/7 SLA with 99.9% uptime | Dedicated engineering team & roadmap planning |
Typical Engagement | Feasibility study & POC | End-to-end system deployment | Strategic partnership for network expansion |
SECURE MULTI-PARTY AI COMPUTATION
Industries and Applications We Serve
Our confidential computing systems enable secure collaboration on sensitive datasets. Organizations can jointly train models and run inferences without exposing their private data, unlocking new value while maintaining strict compliance and security.
01
Financial Services & Fraud Detection
Enable banks and fintechs to collaboratively train fraud detection models on combined transaction data without sharing sensitive customer information. Protect intellectual property for proprietary trading algorithms.
Key Outcomes:
- Jointly improve fraud detection accuracy by 40%+ without data pooling
- Secure execution of quantitative models in attested enclaves
- Comply with GDPR, CCPA, and financial data sovereignty laws
ISO 27001
Compliance
< 5ms
Added Latency
02
Healthcare & Pharmaceutical Research
Facilitate multi-institutional clinical trials and drug discovery by allowing hospitals and pharma companies to train AI on combined patient datasets. Ensure HIPAA/GDPR compliance for data-in-use.
Key Outcomes:
- Accelerate drug discovery timelines by enabling secure data collaboration
- Perform genome-wide association studies across siloed biobanks
- Maintain patient privacy with hardware-enforced data isolation
HIPAA Compliant
Data Processing
Intel SGX
TEE Standard
03
Defense & Geospatial Intelligence
Build secure, multi-party analysis systems for classified satellite imagery, signals intelligence (SIGINT), and threat assessment. Enable allied agencies to collaborate without data leakage risks.
Key Outcomes:
- Federated analysis of sensitive geospatial data across secure networks
- Protect algorithms and models from insider threats and infrastructure compromise
- Deploy air-gapped, hardware-rooted AI for classified processing
FedRAMP High
Authorization
Air-Gapped
Deployment
04
Supply Chain & Logistics Optimization
Allow multiple partners in a supply chain (manufacturers, shippers, retailers) to jointly optimize routing, demand forecasting, and inventory management using their combined operational data confidentially.
Key Outcomes:
- Reduce overall logistics costs through secure, collaborative optimization
- Model end-to-end tariff exposure without exposing proprietary cost structures
- Build trusted digital supply chain twins with multi-party data inputs
99.9%
Uptime SLA
Azure DCsv3
Platform
05
Cross-Border Regulatory Compliance
Engineer systems for multinational corporations to perform AI analytics on region-locked data (e.g., EU citizen data) while contributing insights to global models, ensuring compliance with the EU AI Act and similar mandates.
Key Outcomes:
- Enable global AI initiatives while adhering to strict data localization laws
- Implement technical controls for GDPR 'data in use' requirements
- Facilitate secure model aggregation across sovereign borders
EU AI Act
Compliance
GDPR Art. 32
Alignment
06
Insurance & Risk Modeling
Enable insurers to develop more accurate actuarial models by securely computing on aggregated claims data from multiple carriers. Protect sensitive policyholder information during collaborative AI training.
Key Outcomes:
- Improve risk prediction accuracy through secure data consortiums
- Protect competitive pricing algorithms during joint computations
- Audit all multi-party computations via cryptographically verified attestation reports
SOC 2 Type II
Audited
AMD SEV-SNP
TEE Platform
Technical & Commercial Details
Secure Multi-Party AI Computation FAQs
Get specific answers on timelines, security, and process for our confidential multi-party AI systems.
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01
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