Privacy HyperPersonalization Engine

描述

The Privacy HyperPersonalization Engine (PHE) is a powerful yet ethical API that delivers hyperpersonalized digital experiences without compromising user privacy. In an era where personalization often comes at the cost of invasive data tracking, PHE introduces a new standard—intelligent personalization with zero surveillance.

Leveraging techniques like edge-based inference, differential privacy, and contextual anonymization, PHE allows developers to tailor content, product recommendations, and user flows in real-time—without collecting or storing personally identifiable information.

Key Features:

Privacy-First: No PII stored or shared—ever.
AI-Powered Personalization: Context-aware suggestions driven by user behavior, device context, and anonymized signals.
Plug-and-Play API: Easily integrate with web or mobile apps.
Edge-Optimized: Local inference support for mobile and browser environments.
Flexible Use Cases: From ecommerce and media to healthcare and education.

Impact:

PHE redefines what's possible in AI-driven personalization by aligning with GDPR, Kenya’s Data Protection Act, and global privacy norms. It empowers developers and businesses to build trustworthy AI applications that respect individual rights while delivering engaging, tailored user experiences.

黑客松進展

Core Engine Built: We implemented a privacy-preserving personalization engine using a lightweight machine learning model that runs locally in the browser using TensorFlow.js. This enables real-time recommendations without server-side tracking. Edge Inference Demo: Developed a demo web app that personalizes content based on anonymized browsing behavior (e.g., time spent, scroll patterns) entirely in the browser. No user data leaves the device. Privacy Layer Implemented: Integrated a differential privacy mechanism that introduces noise to aggregated usage metrics, ensuring that even optional telemetry respects anonymity. API Ready: Deployed a RESTful API using FastAPI, enabling server-side personalization using synthetic user profiles (i.e., profiles generated without PII). The API supports use cases like personalized news feeds and adaptive UI. Compliance Dashboard: Built a minimal UI dashboard for developers to manage personalization rules and monitor compliance metrics (e.g., data access logs, consent status). Ongoing Work: Expanding model support to federated learning (e.g., via Flower or PySyft). Adding plug-and-play SDKs for mobile and e-commerce platforms. Preparing open-source documentation for post-hackathon release

Privacy HyperPersonalization Engine

視頻

技術堆疊

描述

黑客松進展

籌資狀態