Two Party Computation Aggregation Using Homomorphic Encryption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing two-party computation (2PC) advertising measurement protocol faces inefficiencies in the aggregation step, particularly when dealing with a high number of subcuts, leading to high processing times and resource usage.
Innovation Solution
The protocol improves efficiency by employing an additively homomorphic encryption scheme to perform the aggregation step non-interactively on encrypted values, and alternatively, using a non-colluding third party to additively share the aggregation, thereby reducing the need for bitwise operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional 2PC protocol with bitwise operations is used for aggregation, then measurement precision is maintained, but processing time and resource usage increase significantly
Solution Approach 1:
The patent replaces the mechanical bitwise operation system with a homomorphic encryption-based computational system. Instead of performing sequential bitwise XOR operations followed by counting, the system uses homomorphic encryption to perform additive operations on encrypted values, which can be parallelized and optimized through cryptographic algorithms, thereby substituting an inefficient mechanical process with a more efficient cryptographic computation process.
Solution Approach 2:
The patent changes the operational parameters of the aggregation process by switching from bitwise operations in plaintext to homomorphic additive operations on encrypted data. This parameter change allows the system to leverage the mathematical properties of homomorphic encryption schemes (such as Paillier or ElGamal) that enable efficient addition of encrypted values without decryption, fundamentally altering the computational approach to improve processing efficiency.
2Productivity
If traditional 2PC protocol with bitwise operations is used for aggregation, then measurement precision is maintained, but resource utilization increases
Solution Approach 1:
The patent replaces the mechanical bitwise operation system with a homomorphic encryption-based computational system. Instead of performing sequential bitwise XOR operations followed by counting, the system uses homomorphic encryption to perform additive operations on encrypted values, which can be parallelized and optimized through cryptographic algorithms, thereby substituting an inefficient mechanical process with a more efficient cryptographic computation process.
Solution Approach 2:
The patent introduces homomorphic encryption as an intermediary layer between the data and the aggregation operation. This intermediary allows computations to be performed on encrypted data without requiring decryption, thereby reducing the computational overhead associated with repeated encryption/decryption cycles and minimizing resource usage while maintaining security and precision.
3Loss of time
If homomorphic encryption is used for aggregation, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies homomorphic encryption schemes that serve multiple functions: they provide confidentiality protection for the aggregated data, enable efficient additive computations on encrypted values, and maintain compatibility with the existing 2PC framework. By selecting encryption schemes like Paillier or ElGamal that naturally support additive homomorphism, the system achieves multi-functionality without requiring separate mechanisms for each operation, thereby managing complexity while improving efficiency.
4Productivity
If third-party involvement is used for additive sharing, then aggregation efficiency improves, but trust requirements increase
Solution Approach 1:
The patent introduces a semi-trusted third party as an intermediary to facilitate the additive sharing of homomorphically encrypted values. This third party performs the aggregation of encrypted data without being able to decrypt it, thereby maintaining security while improving efficiency. The trust requirement is managed by ensuring the third party follows the protocol honestly but does not need to be fully trusted, as the homomorphic encryption properties guarantee that the aggregated result remains encrypted and secure.
Data Source
AI summary
A system includes one or more hardware processors, and at least one memory storing instructions that cause the one or more hardware processors to perform operations comprising receiving, from an online platform system, an advertisement opportunity data comprising a plurality of advert opportunities, and a plurality of opportunity timestamps. The operations additionally include receiving, from an advertiser system, advertisement event data comprising a plurality of events and a plurality of event timestamps, and receiving an attribute result of performing a first two-party computation between the advertiser system and the platform system to attribute the plurality of events to the plurality of advert opportunities based on comparing opportunity timestamps and event timestamps, The operations further include deriving an aggregation result that aggregates the events for a subcut of users based on performing a homomorphic encryption, or on performing a non-trusted third party computation.