Unlock Encrypted Analytics with Vaultree
Vaultree’s analytics capabilities, powered by our VENum homomorphic encryption library, enable analytical computations to run directly on encrypted data — no decryption, no compromise. Built as the analytics layer of the Vaultree Encrypted Data Suite (VEDS), it extends our encryption innovation beyond search and access control to secure AI and data science workflows.
From model training to privacy-preserving predictions on encrypted inputs, Vaultree’s approach keeps your data confidential, your models compliant, and your insights fully usable — all without exposing a single byte of plaintext.
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Enable A New Future With Encrypted Analytics
Become a Secure Data Leader
In Your Industry
Vaultree’s analytics layer — powered by the VENum Fully Homomorphic Encryption (FHE) library — redefines how organisations adopt and scale AI securely. With encrypted model training, encrypted inference, and privacy-preserving evaluation, you can explore advanced analytics and machine learning without ever decrypting your data.
From encrypted regression and classification models to time-series analysis and graph-based learning, Vaultree extends the boundaries of what’s possible — proving that insight and confidentiality can truly coexist. This is the foundation of confidential analytics: where innovation, compliance, and privacy finally work in harmony. Whether developing intelligent automation, predictive insights, or generative systems, Vaultree empowers teams to push boundaries — without breaching trust.
Ready to experiment with our Encrypted Analytics capability?
Explore Vaultree’s VENum and VENumML libraries — open-source foundations for privacy-preserving analytics and machine learning. Build and test encrypted regression, classification, or time-series models while your data stays fully protected.
Our libraries demonstrate the art of the possible with fully homomorphic encryption — giving researchers, developers, and data scientists a hands-on path to experience how encrypted computation will shape the next generation of secure analytics.
Vaultree’s VENum library is advancing the frontiers of encrypted intelligence across industries where privacy, compliance, and collaboration intersect. By enabling distributed analytics across multiple encrypted datasets, organisations can now collaborate securely without moving, centralising, or decrypting data.
See where encrypted analytics is redefining what’s possible
Data Ingestion
Encrypt at the source — before data leaves its origin. Vaultree’s framework allows you to prepare FHE-ready feature encodings for structured, unstructured, or streaming inputs, ensuring your data is protected from the moment it’s created.
Federated Analytics
Analyse distributed, encrypted datasets across multiple organisations or regions without ever centralising or decrypting them. Vaultree’s multi-key cryptography enables each participant to retain control of its data and keys while contributing to secure, aggregated analytics. Insights are shared — data sovereignty is not.
Model Training
Train encrypted regression, classification, or deep-learning models directly on ciphertext using privacy-preserving optimisation methods such as stochastic gradient descent and Nesterov acceleration. Incorporate secure activation functions like sigmoid, tanh, and softmax for advanced experimentation with VENumML.
Real-Time Analysis
Generate insights instantly from encrypted data. Perform encrypted inference, Fast Fourier Transforms, or rolling time-series analytics — all without revealing a single value. Vaultree enables low-latency computation that preserves both utility and confidentiality.
Vaultree’s analytical data processing possibilities
Healthcare & Life Sciences
Facilitates secure collaboration between healthcare providers, researchers, and regulators — enabling AI-driven insights while keeping patient data encrypted and compliant.
- Conduct encrypted clinical and genomic research across hospitals, labs, and regulators — without sharing patient data.
- Train privacy-preserving diagnostic and treatment models collaboratively, maintaining HIPAA and GDPR compliance.
- Run encrypted time-series and outcomes analysis directly on ciphertext.
- Unlock population-scale insights while keeping every patient record confidential.
Financial Services
Empowers financial institutions to perform complex analytics on sensitive data without breaching confidentiality or data residency laws.
- Run encrypted portfolio optimisation, risk assessment, and fraud detection across encrypted customer datasets.
- Enable cross-entity analytics where each institution retains its encryption keys and governance controls.
- Power global AI models that comply with data residency and sovereignty laws.
Legal, Compliance & Governance
Enables privacy-preserving analytics across legal, compliance, and regulatory datasets — ensuring auditability and integrity without revealing underlying evidence.
- Perform analytics and pattern discovery across encrypted case files, evidence repositories, or compliance datasets.
- Preserve confidentiality while enabling inter-agency or cross-border collaboration.
- Maintain immutable, cryptographically signed audit trails of every access and computation.
- Replace trust-based data sharing with cryptographic proof and verifiable control.
Research & Academia
Supports collaborative discovery by allowing institutions to analyse shared datasets while keeping all research data encrypted and sovereign.
- Enable federated, encrypted collaboration between universities, labs, and research consortia.
- Analyse shared datasets without centralising or decrypting them.
- Advance global research while protecting intellectual property and personal data.
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Ready to start your encrypted AI & analytics journey?
The future of AI isn’t just smarter — it’s private, compliant, and sovereign. Vaultree’s Analytics layer, powered by the VENum FHE library, enables organisations to explore AI and machine learning over encrypted data without compromising trust or control.
Whether you’re a researcher, innovator, or enterprise leader, join the next wave of privacy-preserving computation — where insight and confidentiality finally converge.