Privacy-Preserving Social Media Advertising via Homomorphic Aggregation
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Solution Overview
Problem
Existing e-commerce systems face challenges in preserving user privacy and verifying the accuracy of aggregate data, leading to trust issues between influencers, sellers, and users, as they rely on collecting and handling personal information without ensuring secure operation and accurate reporting.
Innovation Solution
The implementation of a method using a network interface and processor to receive and process ciphertexts and signatures from clients, employing homomorphic signature systems and private stream aggregation (PSA) to generate and verify aggregate statistical information while preserving differential privacy, ensuring that the aggregator cannot decrypt individual ciphertexts and preventing data forgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the seller system collects detailed personal information from users to verify sales and provide demographic statistics to the influencer, then the accuracy and completeness of aggregate data is improved, but user privacy is compromised and trust issues arise
Solution Approach 1:
The system segments data processing into two independent pathways: (1) Users submit encrypted individual purchase data to the seller system for accurate tracking and verification, and (2) The seller system generates encrypted aggregate statistics that are verified by the influencer without exposing individual user information. This segmentation allows both precise measurement and privacy protection to coexist.
Solution Approach 2:
The patent introduces cryptographic intermediaries including homomorphic encryption schemes and zero-knowledge proof mechanisms that act as mediators between the seller system and influencer. These intermediaries enable the influencer to verify the accuracy of aggregate sales data without directly accessing or compromising individual user personal information, thus resolving the trust issue while maintaining data accuracy.
2Reliability
If the seller system sends full sale records to the influencer for verification, then the influencer can verify sales accuracy, but a large amount of personal data is divulged and security risks increase
Solution Approach 1:
The system extracts only the necessary verification information from complete sale records. Instead of transmitting full personal data, the seller system generates cryptographic proofs (such as zero-knowledge proofs or homomorphic signatures) that contain only the essential verification elements needed for the influencer to confirm sales accuracy without accessing sensitive personal information.
Solution Approach 2:
The patent transforms the data representation from plaintext personal information to cryptographic parameters including encrypted aggregates and verification proofs. This parameter transformation allows the influencer to verify sales accuracy by checking cryptographic signatures and aggregated values without ever seeing or handling actual personal data, thus eliminating data breach risks while maintaining verification reliability.
3Adaptability or versatility
If the system collects extensive personal information to prove influencer popularity and provide demographic insights, then the influencer can improve marketing effectiveness, but the complexity of secure data handling increases and trust requirements multiply
Solution Approach 1:
The system performs preliminary cryptographic setup and key generation before any data collection occurs. Users are pre-configured with encryption keys and the seller system pre-generates verification parameters and cryptographic schemas. This preliminary action simplifies subsequent data handling by establishing secure frameworks in advance, reducing the complexity of real-time secure processing while enabling versatile marketing analytics.
Solution Approach 2:
The patent replaces traditional mechanical data handling mechanisms (direct data collection, storage, and sharing) with cryptographic mechanisms including homomorphic encryption, zero-knowledge proofs, and secure multi-party computation. This substitution eliminates the need for complex trust-based data management while enabling the influencer to access verified aggregate statistics and demographic insights without handling raw personal information, thus reducing system complexity while maintaining marketing effectiveness.
Data Source
AI summary
A method for operating an aggregator in an electronic commerce system includes receiving ciphertexts and signatures transmitted from multiple clients, each ciphertext encrypting noisy plaintext data of a category of information for a user that purchased a product, generating sums of the ciphertexts and the signatures, verifying the sum of the ciphertexts with a homomorphic signature system based on the sum of the signatures and homomorphic verification data, decrypting the sum of the ciphertexts in a private stream aggregation (PSA) process based on homomorphic decryption data to generate a sum of noisy plaintext data in response to a success of the verification, and identifying aggregate statistical information transmitted from the clients based on the sum of noisy plaintext data while preserving differential privacy of the clients.