SecretBit Ventures builds AI systems that embed cryptographic and privacy‑enhancing techniques such as secure multi‑party computation, homomorphic encryption, and trusted execution environments directly into machine‑learning workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations that need to collaborate on sensitive data—such as proprietary models, confidential network traces, or private codebases—face a trade‑off between leveraging AI capabilities and protecting intellectual property or personal information. Existing AI pipelines often require exposing raw data to centralized services, creating legal, security, and compliance risks.
Solution
SecretBit Ventures creates AI systems that embed cryptographic and privacy‑enhancing techniques directly into the machine‑learning workflow. By combining secure multi‑party computation, homomorphic encryption, and trusted execution environments, the platform enables parties to train, evaluate, and analyze models without revealing underlying data or model parameters. The company designs mathematically rigorous protocols that provide formal security guarantees, ensuring that AI deployments remain trustworthy even in adversarial settings. These solutions are delivered from protocol design through production deployment, supporting use cases such as confidential cross‑organization model testing, privacy‑preserving network trace analysis, and cost‑effective AI‑driven code analysis.
Target Audience
Primary customers are enterprises, research institutions, and AI service providers that require secure collaboration on sensitive datasets or proprietary models, such as cybersecurity firms, AI safety labs, and large technology companies.
Features
- Privacy‑preserving machine learning pipelines using homomorphic encryption and federated learning to keep raw data hidden during training
- Secure multi‑party computation protocols, including wavelet‑transform optimizations, for efficient joint computation without data exposure
- Trusted execution environment (TEE) enclaves for confidential AI model evaluation and cross‑organization testing
- Formal cryptographic protocol design with provable security guarantees and rigorous proof techniques
- Integration support for AI code analysis and LLM deployment optimization that reduces operational costs