Inpher is a pioneering privacy-enhancing technology (PET) platform that enables organizations to securely compute and collaborate on sensitive data without ever exposing it. Using advanced cryptographic techniques like Secure Multi-Party Computation (SMPC) and Federated Learning, Inpher allows data to stay encrypted during processing—ensuring privacy, compliance, and security across jurisdictions and enterprises.
As regulations tighten around how data is shared and used, Inpher provides a secure way for businesses, governments, and research institutions to gain insights from distributed datasets while maintaining full confidentiality. This is especially important in sectors like finance, healthcare, defense, and manufacturing, where privacy concerns and competitive sensitivity make traditional data sharing impossible.
By solving the challenge of analyzing encrypted or siloed data, Inpher unlocks the value of data collaboration without compromising privacy or control.
Features
Secure Multi-Party Computation (SMPC)
Inpher’s patented SMPC engine allows multiple parties to compute jointly on encrypted data without revealing their inputs to each other.
Federated Learning
Trains machine learning models across distributed data sources without moving or exposing the data, enabling compliant AI at scale.
Privacy-Preserving AI
Run predictive analytics and AI models on encrypted data while keeping it protected from unauthorized access and leaks.
Zero Data Movement
No raw data is ever transferred—only encrypted computations and results are shared, eliminating risks associated with data centralization.
Compliance-Ready Architecture
Designed to meet data residency, sovereignty, and privacy regulations like GDPR, HIPAA, and CCPA by keeping data local and secure.
Cross-Enterprise Collaboration
Allows partners, regulators, or internal teams to collaborate securely on joint models or analytics without sharing raw data.
Advanced Cryptographic Protocols
Uses industry-leading privacy-enhancing technologies (PETs), including homomorphic encryption, secret sharing, and SMPC.
Audit and Control Features
Provides full transparency, control, and auditability of who computes on what data, under what terms, and with which permissions.
How It Works
Inpher operates on the principle of compute without exposure. The platform connects to local data sources—such as databases, cloud storage, or data lakes—without requiring the data to leave its original location. Instead, it transforms sensitive data into cryptographically secure representations and distributes these encrypted shares among compute nodes.
When a computation or AI model is executed, each node performs its part of the calculation using SMPC protocols. The final result is decrypted and delivered to authorized users without ever revealing the underlying data to any party—not even Inpher.
Federated learning workflows enable training models across institutions without aggregating data centrally. This enables use cases like training a fraud detection model using data from multiple banks, or building a diagnostic tool across hospitals—while preserving patient confidentiality.
Use Cases
Cross-Border Data Analysis
Organizations with global operations use Inpher to perform compliant analytics on sensitive data across borders without violating data residency laws.
Financial Services & Fraud Detection
Banks collaborate on fraud models or risk assessments using SMPC, ensuring that no one exposes customer data during analysis.
Healthcare Research & Diagnostics
Hospitals and pharma companies use Inpher to run AI models on genomic, diagnostic, or patient data without compromising HIPAA or GDPR compliance.
Secure AI Model Training
Enables AI developers to train machine learning models on private or siloed data across multiple organizations or jurisdictions.
Regulatory Compliance
Allows businesses to run essential analytics while meeting strict data protection standards, including zero trust data sharing principles.
Defense & Government Intelligence
Securely analyze national security data or run collaborative simulations without sharing raw classified information.
Pricing
Inpher offers custom pricing tailored to enterprise needs, given the complexity and specificity of its use cases. Pricing factors include:
Number of data sources and participants
Volume of data processed
Cryptographic features used (e.g., SMPC, federated learning)
Level of enterprise support and integration
On-premise or cloud deployment needs
Organizations interested in evaluating Inpher’s capabilities can request a demo or contact the team for a tailored solution via the official website.
Strengths
Best-in-Class Cryptography
Inpher leads the field in production-grade SMPC and privacy-preserving AI, offering unmatched security for sensitive data collaboration.
No Data Movement
Protects sensitive or regulated data by keeping it at rest and encrypting all computations end-to-end.
Enables Collaboration Without Compromise
Ideal for businesses that want to collaborate on data analysis without violating privacy or compliance obligations.
Supports AI and ML Workflows
Built with machine learning in mind, allowing organizations to train, validate, and infer from models on encrypted datasets.
Global Compliance Support
Designed to address data protection laws across multiple jurisdictions and industries.
Drawbacks
Highly Specialized Platform
Best suited for organizations with complex privacy, AI, or cross-border collaboration needs. May be too advanced for basic compliance use cases.
Requires Technical Expertise
Initial deployment and operation may require strong data science and cryptography support, especially for custom ML use cases.
Custom Pricing Model
No fixed plans or public pricing details make early evaluation or cost planning more difficult for smaller teams.
Comparison with Other Tools
Compared to other privacy-enhancing technologies like Duality, Cape Privacy, or Enveil, Inpher offers a more mature and flexible SMPC engine, proven in real-world enterprise deployments.
Where Duality focuses on homomorphic encryption and Enveil on secure queries, Inpher emphasizes multi-party computation and privacy-preserving AI, enabling broader collaboration scenarios.
It complements tools like DataGrail, BigID, or OneTrust, which focus more on compliance and discovery. Inpher focuses on the processing of sensitive data, rather than just identifying or documenting it.
Customer Reviews and Testimonials
Enterprises working with Inpher often highlight the platform’s ability to unlock secure collaboration with trusted and untrusted parties. Customers in finance and healthcare report improved model accuracy and coverage due to the ability to use more data—without breaching privacy.
Users appreciate the depth of cryptographic protection and the engineering team’s support during implementation. Reviews also mention Inpher’s effectiveness in meeting data residency and sovereignty requirements without sacrificing innovation.
While some note a learning curve, especially around secure compute architecture, the long-term value of privacy-preserving analytics is consistently praised.
Conclusion
Inpher offers a groundbreaking solution for secure data collaboration and privacy-preserving AI. With its advanced use of cryptographic protocols like SMPC and federated learning, the platform enables enterprises to compute on encrypted data without ever exposing it.
In an era where privacy, compliance, and AI development must go hand in hand, Inpher delivers a powerful platform that helps organizations unlock data insights while maintaining full control and confidentiality.
