Homomorphic Encryption Overflow Prevention in ML Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning inference operations with homomorphic encryption often face overflow issues due to limitations in value ranges and activation layers, leading to corrupt inference results and inefficient memory usage.
Innovation Solution
A method that simulates an initial overflow event to determine achievable values, compares them with homomorphic encryption libraries, and adjusts parameters to prevent future overflows, ensuring accurate inference results while optimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homomorphic encryption is used for machine learning inference, then data privacy is preserved during cloud processing, but overflow events occur due to value range limitations
Solution Approach 1:
The system performs a simulation run before actual ML inference to determine the range of values that will be achieved during processing. This preliminary action allows the system to configure appropriate HE library parameters in advance, ensuring that the value ranges are set correctly before real inference begins, thereby preventing overflow events while maintaining privacy preservation.
Solution Approach 2:
The system dynamically adjusts parameters of the homomorphic encryption library based on the simulated value ranges. By changing parameters such as modulus values and polynomial degrees according to the actual data characteristics discovered during simulation, the system ensures that encrypted computations remain within valid value ranges, preventing overflow while maintaining encryption security.
2Reliability
If homomorphic encryption libraries are configured with fixed value ranges, then encryption security is maintained, but memory usage increases due to inefficient resource allocation
Solution Approach 1:
The system optimizes HE library parameters by analyzing actual value ranges from simulation data. Instead of using conservative fixed ranges that waste memory, the system configures parameters tightly matched to the actual computational needs, reducing memory allocation while maintaining encryption security through appropriate parameter selection.
Solution Approach 2:
The system automatically determines optimal memory allocation and HE parameters through self-analysis of the machine learning model and data characteristics during simulation. This self-service approach eliminates the need for manual configuration and ensures efficient memory usage while maintaining security, as the system adapts parameters based on its own analysis of computational requirements.
3Manufacturing precision
If simulation is performed to determine value ranges, then overflow prevention accuracy is improved, but processing time increases due to additional computational steps
Solution Approach 1:
The system performs simulation on a subset of representative data or uses simplified model versions to estimate value ranges, rather than exhaustively simulating all possible inputs. This partial action approach provides sufficient accuracy for overflow prevention while significantly reducing the time cost compared to complete simulation, achieving a practical balance between precision and efficiency.
Data Source
AI summary
Identifying and avoiding an overflow event while performing machine learning inference operations with homomorphic encryption. Prior to a first run of a machine learning inference operation, a first overflow event is created in order to determine the values that are achieved values. These values are compared to a set of user selected homomorphic encryption libraries in order to determine which parameters of the machine learning inference operation must be adjusted in order to avoid future overflow events during subsequent runs of the machine learning inference operation.