Cipher Analytics

Cipher Analytics — Privacy-Preserving Distributed AI Platform

Cipher Analytics is a security-first, high-throughput analytics system designed to enable secure machine learning computation and statistical aggregation directly on encrypted datasets without exposing raw user data.

Core Architecture & Technical Highlights

• Homomorphic Cryptography: Implemented privacy-preserving computing architectures using Fully Homomorphic Encryption (FHE) with the CKKS scheme (via TenSEAL/SEAL libraries), supporting secure ML inference.
• Distributed Processing Pipeline: Designed a multi-queue distributed task architecture using Celery and Redis to isolate high-compute cryptographic functions from standard web I/O workloads, improving latency and preventing core bottlenecks.
• Event-Driven Backend: Built an asynchronous, real-time feedback backend using Django REST Framework, Django Channels (WebSockets), and Daphne to stream live task progress and FHE computation results.
• Interactive 3D Analytics: Engineered a real-time statistical visualization layer using WebGL (Three.js and React Three Fiber) and ECharts to map high-dimensional clusters and anomalous data patterns.
• Clustering & AI Explainability: Applied SHAP (SHapley Additive exPlanations) for local model explanation, and integrated UMAP and HDBSCAN algorithms to cluster and identify anomalies in encrypted datasets.
• Production-Grade Security: Orchestrated services using Docker with secure shared-volume mounts. Enforced strict rate-limiting, comprehensive audit logging (via django-simple-history), static typing (mypy), and security scanning (bandit).