Xcapit Privacy: Machine Learning Without Seeing Your Data

In a world where data is the most valuable asset for artificial intelligence, a fundamental paradox exists: the best ML models require large, diverse datasets—but the most sensitive data can never be shared. Financial institutions can't pool transaction data for fraud detection. Hospitals can't share patient records for medical research. Insurers can't collaborate on risk models without exposing their portfolios. Until now, organizations had to choose between AI quality and data privacy.

What is Xcapit Privacy

Xcapit Privacy is a platform developed by Xcapit Labs that enables organizations to perform machine learning on data that remains encrypted at all times. Using Fully Homomorphic Encryption (FHE), the platform allows mathematical computations—addition, multiplication, and by extension any ML algorithm—to be performed directly on ciphertext. The results, when decrypted by the data owner, are identical to what would have been obtained on the plaintext data.

This means organizations can contribute data to collaborative ML models, receive predictions and insights, and benefit from the collective intelligence of multiple datasets—all without any party ever seeing another's data. Not even Xcapit Privacy itself can access the underlying information.

How FHE Works

Fully Homomorphic Encryption (FHE) is a cryptographic technique that allows computations on encrypted data without decrypting it first. Xcapit Privacy uses the CKKS scheme, which is optimized for approximate arithmetic on real numbers—making it ideal for machine learning workloads.

The process works as follows: (1) The data owner encrypts their data locally using their private key and 256-bit security parameters. (2) The encrypted data is sent to the platform, where ML algorithms operate directly on the ciphertext. (3) The encrypted results are returned to the data owner, who decrypts them with their private key. At no point does the raw data leave the owner's environment unencrypted.

Key Features

Always Encrypted

Data is encrypted before it leaves the owner's infrastructure and remains encrypted throughout the entire processing pipeline. There is no decryption step on the server side—ever. This eliminates the attack surface that exists in traditional cloud ML approaches.

Private Machine Learning

The platform supports 15+ machine learning algorithms including linear regression, logistic regression, decision trees, random forests, SVM, k-means clustering, PCA, and neural networks—all operating on encrypted data. Model training and inference produce results mathematically equivalent to plaintext processing.

Secure Collaboration

Multiple organizations can contribute encrypted datasets to train shared models. Each participant benefits from the combined data volume and diversity without exposing their individual records. This is particularly powerful for industries where data sharing is legally restricted but collective insights would be valuable.

Automatic Compliance

Because data is never exposed in plaintext, Xcapit Privacy inherently satisfies data protection requirements under GDPR, HIPAA, SOC 2, PCI-DSS, and other frameworks. Organizations can demonstrate to regulators that personal data was never accessible—even to the processing platform.

Blockchain Governance

All data contributions, model training events, and result distributions are recorded on the Arbitrum blockchain. This provides an immutable audit trail that proves data was handled according to agreed-upon rules, without revealing the data itself. Smart contracts enforce access policies and data usage agreements.

Federated Inference

Trained models can be used for predictions on new encrypted data without re-training. Organizations can query the shared model with their private data and receive encrypted predictions that only they can decrypt.

Use Cases

Financial Services

Banks and fintech companies can collaboratively train fraud detection models using transaction data from multiple institutions—without any bank seeing another's customer transactions. The combined model outperforms any single institution's isolated model.

Healthcare

Hospitals and research centers can contribute patient data to train diagnostic models while maintaining full HIPAA compliance. Medical imaging analysis, drug interaction prediction, and epidemiological studies become possible across institutional boundaries.

Insurance

Insurers can pool risk assessment data to build more accurate actuarial models without exposing proprietary portfolio information. Claims prediction, pricing optimization, and fraud detection all benefit from larger, more diverse training datasets.

Government

Government agencies can share intelligence data for threat detection and public safety analysis while maintaining classification levels and inter-agency data sharing restrictions.

Technology Stack

Xcapit Privacy is built on TenSEAL, an open-source library for homomorphic encryption operations, implementing the CKKS scheme for approximate arithmetic on encrypted real numbers. The backend uses FastAPI (Python) for ML pipeline orchestration, with PostgreSQL for metadata management. The governance layer runs on Arbitrum (Ethereum L2) for cost-efficient blockchain transactions. The user-facing dashboard is built with React, providing 42 pages for data management, model configuration, results visualization, and audit trail exploration. The entire platform is containerized with Docker for deployment flexibility.

Comparison with Alternatives

Traditional ML requires centralized plaintext data, creating massive security and compliance risks. Multi-Party Computation (MPC) distributes computation across parties but requires all parties to be online simultaneously and scales poorly with more participants. Federated Learning keeps data local but shares model gradients, which have been shown to leak information about the training data. Differential Privacy adds noise to protect individual records but degrades model accuracy. FHE, as used by Xcapit Privacy, is the only approach that provides mathematical guarantees of privacy while maintaining full computational accuracy.

Quality & Testing

The platform has been developed with a rigorous testing methodology: 559+ automated tests covering unit tests, integration tests, and end-to-end scenarios. Every ML algorithm is validated by comparing encrypted computation results against plaintext baselines, ensuring mathematical equivalence within CKKS approximation bounds.

Key Takeaways

• FHE enables machine learning on data that remains encrypted throughout the entire computation pipeline
• Organizations can collaborate on ML models without ever exposing their sensitive data
• The CKKS scheme supports 15+ ML algorithms with results mathematically equivalent to plaintext processing
• Blockchain governance provides an immutable audit trail without revealing the underlying data
• Inherent compliance with GDPR, HIPAA, SOC 2, and PCI-DSS—data is never accessible in plaintext
• 559+ automated tests ensure mathematical correctness and system reliability