Homomorphic Recommendation Engine for Privacy-Preserving Similarity Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recommendation systems on social media platforms compromise user privacy by analyzing and decrypting personal data to generate recommendations, which can lead to misuse and loss of control over user data.
Innovation Solution
A privacy-protected recommendation engine using Fully Homomorphic Encryption (FHE) processes encrypted user data without decryption, allowing third-party services to generate recommendations based on encrypted inputs, ensuring user data remains secure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user data is decrypted to generate recommendations, then recommendation quality is improved, but user privacy is compromised
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary mechanism that allows the recommendation system to process encrypted user data without decryption. The encrypted data serves as a mediator between the user's privacy requirements and the system's analytical needs, enabling computations on ciphertext while preserving data confidentiality throughout the processing pipeline.
Solution Approach 2:
The system transforms the data state parameter from decrypted to encrypted form, maintaining data in encrypted state throughout the recommendation generation process. This parameter change enables the system to perform similarity computations and generate recommendations while the data remains in a protected encrypted state, thus preserving privacy without sacrificing recommendation functionality.
2Object-affected harmful factors
If user data is kept encrypted, then user privacy is protected, but processing capability is reduced
Solution Approach 1:
The patent replaces the traditional mechanical approach of decrypting data for processing with a cryptographic substitution approach using homomorphic encryption. Instead of converting encrypted data back to plaintext for analysis, the system performs computations directly on the ciphertext, substituting the decryption-processing-encryption workflow with an end-to-end encrypted processing pipeline that maintains productivity while enhancing security.
3Ease of manufacture
If recommendation processing is outsourced to third parties, then company costs are reduced, but data security risks increase
Solution Approach 1:
Homomorphic encryption serves as a trusted intermediary mechanism that enables secure outsourcing of recommendation processing. The encryption scheme acts as a mediator between the company's data security requirements and the third-party processor's analytical capabilities, allowing the third party to process data without accessing plaintext information, thus reducing company costs while maintaining data security through cryptographic guarantees.
Data Source
AI summary
An approach is disclosed that receives a request from a requestor over a computer network, the request including a set of encrypted user data that includes interest vectors pertaining to a set of users. A homomorphic encryption processing of the encrypted user data is performed using a distance function that utilizes an encrypted lookup table. This results in a set of distance results. An encrypted distance array is then generated from the distance results. The encrypted distance array describes similarities between the users in the set of encrypted data. A set of encrypted recommendations are returned to the requestor. The recommendations returned are derived from the encrypted distance array.