What is MPC for Data Collaboration?

Organizations hold valuable data. Banks have transaction histories. Payment networks have cross-border flow data. Healthcare systems have patient records. Insurance companies have claims data. ms hold records for billions of patients across institutions. Insurance companies maintain decades of claims data. Each organization's data becomes far more valuable when combined with data from other organizations. Joint fraud models trained across multiple banks outperform single-institution models. Cross-border credit assessments require data from multiple jurisdictions. Drug interaction studies need patient data from multiple hospitals.

The problem is simple: no organization wants to share its raw data with another organization. Regulatory constraints prevent it. Competitive concerns prevent it. Privacy obligations prevent it. The data stays locked inside organizational boundaries, and the insights that could come from combining it are never realized.

Multi-Party Computation (MPC) for data collaboration solves this problem. It enables multiple organizations to jointly compute analytics, train models, and derive insights from their combined datasets without any organization exposing its raw data to the others.

How MPC for Data Collaboration Works

In a traditional data collaboration, organizations send copies of their data to a central location. A trusted third party or a shared database holds the combined dataset. Analytics run on the combined data. This model requires trust in the central party, exposes raw data to that party, and creates a high-value target for attackers.

MPC eliminates the central party. Each organization keeps its data on its own infrastructure. When a joint computation is needed, MPC protocols distribute the computation across the participating organizations. Each organization processes its portion of the computation on its own data. The intermediate results are cryptographically protected using techniques like secret sharing. Only the final output is revealed. No organization sees any other organization's raw data at any point in the process.

The core principle: data never moves. Only inferences do.

This is fundamentally different from approaches that move encrypted data to a central location and compute on it there (as in some homomorphic encryption deployments). With MPC, the data stays where it is. The computation goes to the data.

MPC vs Other Privacy-Preserving Technologies

Several technologies enable privacy-preserving data collaboration. Each takes a different approach.

Fully Homomorphic Encryption (FHE)

FHE allows computation on encrypted data without decryption. One party encrypts its data, sends it to another party, and that party runs computations on the encrypted data and returns encrypted results. FHE is powerful but introduces significant computational overhead. Benchmarks from Zama's TFHE library show 1,000x to 10,000x slowdowns compared to plaintext computation for typical ML workloads. FHE also typically involves one party sending its encrypted data to another, which means data does move between organizations.

Federated Learning

Federated learning trains machine learning models across distributed datasets. Each organization trains a local model on its own data and sends model updates (gradients) to a central aggregator. The aggregator combines the updates into a global model. Federated learning is effective for model training but has known vulnerabilities. Model gradients can leak information about the underlying training data through gradient inversion attacks. The aggregator also becomes a coordination bottleneck.

Trusted Execution Environments (TEEs)

TEEs create hardware-isolated secure regions within processors. Data can be processed inside a TEE without the host operating system or cloud provider being able to access it. TEEs offer strong performance but depend on hardware vendor trust. Side-channel attacks against Intel SGX and other TEE implementations are well-documented in the research literature.

Data Clean Rooms

Data clean rooms provide controlled environments where organizations can run approved queries on combined datasets. Clean rooms are widely used in advertising and marketing. They enforce access controls and query restrictions but rely on the clean room operator to enforce privacy. The cryptographic guarantees are weaker than MPC.

Where MPC Fits

MPC provides the strongest privacy guarantees for multi-party computation. No trusted third party is required. No data leaves the organization. The security is based on mathematical proofs, not on trust in a hardware vendor or platform operator. The tradeoff is that MPC introduces communication overhead between participating parties. For many enterprise use cases, particularly in regulated industries where the privacy guarantees matter most, this tradeoff is acceptable.

Use Cases for MPC-Based Data Collaboration

Cross-Bank Fraud Detection

Chainalysis reported $3.4 billion stolen from crypto platforms in 2025 alone. A significant portion of fraud involves transaction patterns that span multiple institutions. No single bank can detect these cross-institutional patterns from its own data.

Multiple banks can jointly train fraud detection models on their combined transaction data without any bank seeing another bank's transactions. The Bank for International Settlements explored this approach in Project Mandala, a cross-border compliance framework that uses privacy-preserving technology to enable joint analytics without raw data exposure. Silence Laboratories contributed the privacy-preserving computation layer for Project Mandala's sanction screening and capital flow management functions.

Cross-Border Credit Assessment

Lending institutions in different jurisdictions can compute credit risk assessments using data from multiple sources without transferring personal financial data across borders. This is particularly relevant for MSME (micro, small, and medium enterprise) lending where credit data is fragmented across countries.

Silence Laboratories won the G20 TechSprint award for building exactly this use case with Proxtera, enabling privacy-preserving MSME inference for cross-border underwriting.

Anti-Money Laundering Consortiums

Banks can jointly screen transactions against sanctions lists and identify suspicious patterns across institutions without sharing customer data. MPC enables the detection of network-level money laundering patterns that no single bank can see from its own data alone.

Healthcare Research

Hospitals and research institutions can jointly analyze patient data for drug interaction studies, epidemiological research, and clinical trial matching without sharing individual patient records. MPC ensures HIPAA compliance while enabling multi-site research.

Financial Benchmarking

Industry participants can compute aggregate statistics (average salaries, market benchmarks, pricing comparisons) without any participant revealing its individual data. Each participant learns the aggregate result but nothing about any other participant's contribution.

Open Finance and Account Aggregation

Financial data from multiple sources can be computed on for underwriting, risk assessment, and personalized financial products without centralizing raw financial records. MPC enforces consent and governance cryptographically, ensuring data is used only for the approved computation.

How Silence Laboratories Enables MPC-Based Data Collaboration

Silence Laboratories builds Silent Compute, a privacy-preserving computation platform designed for cross-institutional data collaboration.

Silent Compute uses the Cryptographic Computing Virtual Machine (CCVM), a modular runtime that lets organizations build verifiable, policy-enforced applications on distributed data without touching raw data. The CCVM supports secure statistics, secure machine learning, secure network graphs, and privacy-preserving inference.

Three deployment constructs are available depending on the collaboration model:

Two-Party Compute. Two organizations jointly compute on their combined data. Each organization holds one share of the computation. Suitable for bilateral collaborations like bank-to-bank fraud analysis or insurer-to-insurer risk pooling.

Multi-Party Compute. Three or more organizations participate in a joint computation. Each organization holds one share. Suitable for industry consortiums like anti-fraud networks or cross-border lending platforms.

Delegated Analytics. A trusted analytics provider runs computations on behalf of data-holding organizations without ever seeing the raw data. Suitable for scenarios where a regulator or industry body needs aggregate analytics across multiple institutions.

Security and Compliance

Silent Compute inherits the cryptographic rigor of Silence Laboratories' broader infrastructure. The cryptographic libraries are audited by Cure53, Trail of Bits, HashCloak, and Secfault Security across eight independent engagements.

The platform supports GDPR-compliant analytics without raw data exposure. GDPR fines have exceeded €4.5 billion cumulatively since enforcement began in 2018. HIPAA violations have resulted in over $130 million in penalties in the US alone. For institutions operating across jurisdictions, the compliance cost of raw data sharing often exceeds the analytical value. MPC eliminates this tradeoff. Consent and governance are enforced cryptographically at the computation layer. Each computation is auditable. Each data use is traceable to a specific approved purpose.

Enterprise Readiness

Silence Laboratories is a G20 TechSprint winner in the Trust and Integrity in Scalable and Open Finance category. The company is a General Member of the Linux Foundation Decentralized Trust. The founding team includes PhD researchers from MIT, NUS, UIUC, and SUTD. The VP of Cryptography, Yashvanth Kondi, co-invented the DKLs protocol series that underpins the company's MPC implementations.

Why MPC for Data Collaboration Matters Now

Two forces are converging. Regulations like GDPR, HIPAA, and CCPA make raw data sharing increasingly difficult and risky. At the same time, the value of cross-institutional analytics is growing as AI models improve with more diverse training data and as financial regulators demand more comprehensive risk visibility across institutions.

MPC resolves the tension between these two forces. It enables the analytics without the data sharing. Organizations can collaborate on data-driven insights while maintaining full control over their raw data and full compliance with privacy regulations.

The organizations that adopt MPC-based data collaboration first will build analytical advantages their competitors cannot match, because the insights require cross-institutional data that traditional data-sharing agreements cannot safely provide.