Privacy-Aware In-Network Personalization via Request Key Embedding
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Solution Overview
Problem
Current personalization systems in communication networks lack efficiency, effectiveness, and privacy preservation, preventing network operators from participating in the growing personalization economy.
Innovation Solution
A privacy-aware in-network personalization system that uses a preprocessing component to generate request keys, a categorization component to map these keys to domain-dependent vocabulary, an augmentation and buffer component to augment and buffer request sequences, and an embedding model component to update a machine learning model for real-time personalization, without storing user-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user-specific data is stored for personalization, then personalization accuracy is improved, but user privacy is compromised
Solution Approach 1:
The patent extracts only the essential features and patterns from user data that are needed for personalization, while removing or not storing the actual user-specific information. The embedding model learns from aggregated request sequences without retaining identifiable user data, thus achieving personalization accuracy while preserving privacy.
Solution Approach 2:
The patent introduces an embedding model as an intermediary that transforms raw user request data into abstract vector representations. This intermediary layer enables personalization computations without directly handling or storing sensitive user information, effectively mediating between data utilization and privacy protection.
2Measurement precision
If comprehensive user data is collected for personalization, then personalization quality is improved, but computational resources and memory consumption increase
Solution Approach 1:
The patent changes the parameter representation from raw user data to compressed embedding vectors. By transforming user request sequences into fixed-dimensional vector representations, the system maintains personalization quality while significantly reducing memory and computational resource requirements for processing and storing user information.
Solution Approach 2:
The patent creates simplified copies of user data in the form of embedding vectors that capture essential patterns without replicating the full complexity of original user request sequences. These vector representations serve as efficient proxies that enable personalization with reduced resource consumption.
3Speed
If user request sequences are processed in real-time, then personalization responsiveness is improved, but processing complexity increases
Solution Approach 1:
The patent performs preliminary processing of user request sequences by pre-computing embedding vectors and maintaining buffers of recent requests. This preliminary action prepares data structures in advance, enabling fast real-time personalization decisions without complex processing during actual request handling.
Solution Approach 2:
The patent segments the personalization process into distinct components: request sequence buffering, embedding model updates, and personalization decision-making. This segmentation allows each component to be optimized independently, reducing overall processing complexity while maintaining real-time responsiveness.
Data Source
AI summary
A personalization system includes a preprocessing component configured to receive a request from a user over a communications network and generate a request key using predefined attributes of the request. A categorization component is configured to map the request key to a subset of domain-dependent vocabulary. An augmentation and buffer component is configured to augment the request with the subset of domain-dependent vocabulary mapped to the request key by the categorization component and to buffer request sequences in queues according to sequence identifiers. An embedding model component is configured to update an embedding model using the buffered request sequences. A personalization component is configured to provide a personalization using the updated embedding model.