Private Computing Infrastructure for Enterprises
A guide to the technologies that keep enterprise data protected during use.
Written by
Berwin D
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Jun 27, 2026
Private computing infrastructure is the hardware, software, and cryptographic systems that allow organizations to process, analyze, and collaborate on sensitive data without exposing it. The data remains encrypted or distributed during computation. Only the outputs are revealed.
This is different from encrypting data at rest (stored on disk) or in transit (moving across a network). Private computing protects data in use, during the moment of computation itself. That distinction matters because most data breaches and regulatory violations occur when data is decrypted for processing.
This guide covers the five main technologies in the category, explains how they compare, and maps them to enterprise use cases in finance, healthcare, and digital assets.
The Problem: Data Is Vulnerable During Computation
Standard enterprise data processing requires decryption. A database query, an ML training run, a fraud screening check, a financial analytics report: all of these require the data to exist in plaintext in memory at some point during execution. That window of plaintext exposure is the attack surface.
For a single organization processing its own data on its own servers, the exposure is manageable through access controls, network segmentation, and monitoring. For cross-institutional collaboration, the exposure becomes structural. If Bank A wants to check whether a customer appears on Bank B's sanctions list, standard approaches require one party to share data with the other. The data leaves the owner's control.
Approximately 80% of countries now have active or draft privacy legislation, and global spending on privacy-compliance tools is expected to reach $8 billion. The regulatory environment now penalizes plaintext data exposure, cross-border data transfers, and data processing that exceeds consented scope. Private computing infrastructure addresses all three by eliminating the need for data to exist in accessible form during processing.
The Five Core Technologies
Multi-Party Computation (MPC)
MPC distributes a computation across multiple parties who each hold a share of the input data. The computation produces a result without any party seeing the other parties' inputs. The output is revealed; the inputs remain private.
MPC is the foundation of two distinct enterprise use cases. In threshold key management, MPC distributes a cryptographic private key into shares so that no single device or server holds the complete key. In data collaboration, MPC enables multiple institutions to compute jointly on data that never leaves their respective environments.
Strengths: No trusted third party required. Mathematically provable privacy guarantees. Works across organizational boundaries. Outputs can be cryptographically verified.
Tradeoffs: Communication overhead between parties. Requires all parties to be online during computation. Performance depends on the specific protocol and the complexity of the computation.
Enterprise applications: Digital asset custody, cross-institutional fraud detection, sanctions screening, privacy-preserving analytics, distributed key management.
Fully Homomorphic Encryption (FHE)
FHE allows computation on encrypted data without decrypting it. A party can send encrypted data to a server, the server performs operations on the ciphertext, and the result is returned still encrypted. Only the data owner can decrypt the output.
Strengths: The computing party never sees the data in plaintext. Strong fit for scenarios where even the computation environment cannot be trusted.
Tradeoffs: Significant computational overhead compared to plaintext operations. FHE computation can be orders of magnitude slower than equivalent plaintext processing. Performance is improving but remains a constraint for latency-sensitive applications.
Enterprise applications: Encrypted database queries, ML inference on regulated health data, cloud analytics on sensitive financial data.
Trusted Execution Environments (TEEs)
TEEs are hardware-isolated enclaves within a processor that provide a protected execution environment. Code and data inside the enclave are isolated from the operating system, hypervisor, and other software on the machine. Intel SGX, ARM TrustZone, and AWS Nitro Enclaves are examples.
Strengths: Near-native performance. The computation runs at close to full processor speed because it operates on plaintext within the enclave. Well-supported by major cloud providers.
Tradeoffs: Requires trust in the hardware manufacturer. Side-channel attacks against TEEs have been documented (Spectre, Meltdown, and related variants). The security guarantee depends on hardware integrity, not mathematical proof.
Enterprise applications: Confidential cloud computing, secure key storage, isolated processing of regulated data within cloud environments.
Differential Privacy
Differential privacy adds calibrated noise to query results or datasets so that individual records cannot be identified from the output, while the statistical properties of the dataset are preserved. It is a mathematical framework for quantifying privacy loss.
Strengths: Strong theoretical guarantees for aggregate analytics. Well-suited for publishing statistics or training ML models without exposing individual records.
Tradeoffs: Does not protect data during computation itself. The noise added reduces the precision of results. Not applicable to use cases requiring exact outputs (key management, transaction signing, identity verification).
Enterprise applications: Privacy-preserving analytics, census and survey data publication, ML model training with privacy guarantees, adtech audience measurement.
Federated Learning
Federated learning trains ML models across distributed datasets without centralizing the data. Each participating node trains a local model on its own data and shares only model updates (gradients) with a central aggregator. The raw training data never leaves the local node.
Strengths: Enables ML training across data silos without data movement. Well-suited for healthcare, finance, and any domain where data cannot leave its source.
Tradeoffs: Model updates can leak information about the underlying training data (gradient inversion attacks). Requires additional privacy techniques (differential privacy, secure aggregation) to provide strong guarantees.
Enterprise applications: Cross-institutional ML model training, healthcare predictive models, financial risk models trained across institutional boundaries.
How the Technologies Compare
Technology | Protects Data In Use | Requires Trusted Hardware | Performance Overhead | Mathematically Provable | Multi-Party |
|---|---|---|---|---|---|
MPC | Yes | No | Moderate | Yes | Yes |
FHE | Yes | No | High | Yes | Single-party computation |
TEE | Yes (hardware-isolated) | Yes | Low | No (hardware trust) | No |
Differential Privacy | No (protects outputs) | No | Low | Yes (statistical) | No |
Federated Learning | Partial (data stays local) | No | Moderate | No (without additions) | Yes |
These technologies are often combined. MPC can run inside TEEs for defense-in-depth. Federated learning can be combined with differential privacy to protect gradient updates. The right combination depends on your threat model, performance requirements, and regulatory context.
What "Inference Sharing" Means
Inference sharing is the principle that organizations share the results of computations, not the data that produced them. Two banks want to know whether a customer appears in both their fraud databases. The answer is a yes/no inference. Neither bank needs to see the other's full customer list to get that answer.
This is the core architectural principle behind private computing for data collaboration. It is distinct from data sharing with contractual restrictions, where the data moves but is governed by agreements. It is also distinct from anonymization, where data is modified before sharing. In inference sharing, the raw data never moves at all. Only the computation output is revealed.
Silence Laboratories builds its product suite around this principle. Silent Compute uses MPC to enable cross-institutional analytics, fraud detection, and sanctions screening where data stays at the source and only inferences are shared. Silent Shard uses MPC for threshold key management, where the "inference" is a cryptographic signature produced from distributed key shares without the full key ever existing.
How Silence Laboratories Maps to Enterprise Use Cases
Silence Laboratories builds MPC infrastructure for two categories: key management and data collaboration. The company is headquartered in Singapore and was founded by PhD-trained cryptographers from MIT, NUS, UIUC, and IITs.
Key Management: Silent Shard
Silent Shard is a threshold signature SDK implementing the DKLs23 protocol for distributed key management across digital asset custody, wallet infrastructure, and payment systems.
Production deployments include BitGo (institutional custody), Okto by CoinDCX (12 million wallets), and Easy Crypto (New Zealand's largest crypto retailer). The SDK has been audited by Trail of Bits, Cure53, HashCloak, and Secfault across 8 independent engagements. The open-source DKLs23 implementation is available for independent review.
For deeper comparisons of key management approaches, see MPC vs Multisig and Best MPC Wallet SDK for Developers.
Data Collaboration: Silent Compute
Silent Compute is the privacy-preserving computation platform. It operates on a Cryptographic Computing Virtual Machine (CCVM), a modular runtime for building verifiable, policy-enforced applications on data that each party controls.
The CCVM supports use cases including cross-border compliance (sanctions screening, capital flow management), fraud detection across institutional boundaries, and privacy-preserving financial analytics. Silence Laboratories participated in BIS Project Mandala, a cross-jurisdictional case study in privacy-preserving compliance. The company won the G20 TechSprint 2025 with Proxtera for a cross-border MSME credit inference engine.
A Privacy-Preserving Financial Analytics Toolkit is available on the Databricks Marketplace, enabling institutions to run sanctions screening and transaction monitoring through MPC-based Private Set Intersection without exposing raw data.
Post-Quantum: Future-Proofing Cryptographic Infrastructure
NIST finalized post-quantum standards with classical algorithm deprecation by 2030. Silence Laboratories offers Post Quantum MPC, post-quantum MPC wallet infrastructure. BitGo completed the first post-quantum MPC transaction simulation by a regulated custodian in collaboration with Silence Laboratories.
How to Evaluate Private Computing Infrastructure
Silence Laboratories offers a public PET Assessment Framework to help enterprises determine which privacy-enhancing technology matches their use case. The key evaluation dimensions are:
Threat model. Who are the adversaries? What data must be protected, and from whom? If you are protecting against external attackers, TEEs may be sufficient. If you are protecting data from other computation participants (cross-institutional scenarios), MPC or FHE is required.
Performance requirements. How fast does the computation need to be? MPC threshold signatures run in sub-20ms. FHE queries carry significant overhead. TEEs run at near-native speed. Match the technology to your latency tolerance.
Deployment model. Does the solution deploy inside your infrastructure, or does it require a managed SaaS layer? Regulated institutions often require on-premises deployment. Silent Shard and Silent Compute both support on-premises SDK deployment.
Regulatory context. Which privacy frameworks govern your operations? GDPR, MAS TRM, HIPAA, India's DPDP Act, and similar frameworks each have specific requirements for data processing, transfer, and consent. Private computing infrastructure can address these requirements structurally, but the specific technology must be matched to the regulatory framework.
Post-quantum readiness. Will your cryptographic infrastructure need to remain secure against quantum-capable adversaries within the next decade? If yes, your vendor must have a post-quantum migration path.
Related Reading
Privacy-Preserving Computation Alternatives: Inpher, Duality, and Beyond
Best Privacy-Preserving Computation Platforms for Enterprises
Anti-Fraud Consortium: Privacy-Preserving Fraud Signal Sharing
FAQ
What is private computing? Private computing is the use of cryptographic or hardware-based technologies to process data without exposing it. The data remains encrypted or distributed during computation. Only the output is revealed. This protects data "in use," which is the gap that encryption at rest and encryption in transit do not cover.
What is the difference between MPC and FHE? MPC distributes a computation across multiple parties who each hold shares of the data. No party sees the others' inputs. FHE allows computation on data that remains fully encrypted, performed by a single party. MPC is better suited to multi-party collaboration. FHE is better suited to single-party processing of encrypted data.
What is inference sharing? Inference sharing means sharing the result of a computation without sharing the data that produced it. Two institutions compute jointly on each other's data, but only the output (the inference) is revealed. The raw data never leaves either institution's environment.
How does Silence Laboratories' technology work? Silence Laboratories builds two product lines on MPC. Silent Shard distributes cryptographic key shares across devices and servers for threshold signing in custody and wallet applications. Silent Compute enables cross-institutional data collaboration where institutions compute on each other's data without either party seeing the underlying inputs. Both products enforce inference sharing: data stays at the source, only outputs are revealed.
Which industries use private computing? Financial services (custody, cross-border compliance, fraud detection, analytics), healthcare (privacy-preserving patient analytics, cross-institutional research), digital assets (wallet security, custody infrastructure), and government (privacy-preserving regulatory compliance, cross-jurisdictional data sharing).
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