Triple Blind

Triple Blind has presented 1 V-Sum technical briefing. TripleBlind demos a cryptographic platform for privacy-preserving data and algorithm exchange — an alternative to the status-quo model of decrypting and replicating third-party data (the Capital One / Experian problem). TripleBlind\'s novel one-way encryption is homomorphic-like but roughly a trillion times faster than full homomorphic encryption, and it works across text, voice, video, and images. The data can only be used for the purpose explicitly authorized, and algorithms themselves can also be encrypted — protecting IP and training data from reverse-engineering. The live demo uses Kaggle\'s customer-transaction-prediction dataset split across three mock organizations (Standard Chartered, JPMorgan Chase, BNP Paribas) running in three different browsers. Privacy-preserving EDA lets a data scientist see shape, distribution, and plots without seeing individual records. A feed-forward neural network (dense + ReLU layers) is trained across all three parties — each granting cryptographic consent with a justification — and produces a privacy-preserving PyTorch object that can be used for local inference or secure multi-party compute predictions (both algorithm and data stay hidden from each other). Training runs in minutes where FHE would take weeks, and the code is drop-in compatible with PyTorch, Pandas, TensorFlow, and XGBoost — GDPR, CCPA, and FDIC compliant.

Topics: Data Infrastructure, Compliance & Regulation