Encrypted Decision Tree Model for Confidential Edge Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models in AI services are vulnerable to man-in-the-middle attacks and model inversion attacks, especially in edge computing contexts where lightweight security measures are computationally challenging, and there is a need for confidential inference solutions that protect both the model and client data privacy.
Innovation Solution
A system and method that utilize an encrypted decision tree model executed on an accelerator, employing fully homomorphic encryption or order-preserving cryptography to evaluate encrypted data without decrypting it, ensuring privacy and security by using a host device and a user device with an agreed-upon encryption schema, and implementing the model on hardware accelerators like FPGAs for edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption methods are used to protect machine learning models, then security against man-in-the-middle attacks is improved, but computational efficiency deteriorates due to the heavy computational burden of decryption operations
Solution Approach 1:
The patent replaces traditional decryption-based security mechanisms with homomorphic encryption, allowing computational operations to be performed directly on encrypted data without decryption. This substitution eliminates the computational burden of decryption while maintaining security, as the encrypted data can be processed in its encrypted state through specialized hardware accelerators.
Solution Approach 2:
The patent changes the cryptographic parameter state by using homomorphic encryption schemes that preserve computational properties in the encrypted domain. Instead of requiring data to be decrypted for processing, the system transforms the encryption parameters to allow mathematical operations on ciphertexts, fundamentally changing how security and computation interact.
2Speed
If data is decrypted for processing in machine learning models, then computational speed is improved, but data privacy and model security deteriorate due to exposure to attacks
Solution Approach 1:
The patent replaces the traditional decrypt-then-process workflow with a process-while-encrypted workflow using homomorphic encryption. This allows the system to maintain computational speed through hardware acceleration while preserving data privacy, as the data remains encrypted throughout the entire processing pipeline and only the final result needs decryption.
3Ease of operation
If lightweight security measures are implemented in edge computing, then ease of operation is improved, but security effectiveness deteriorates due to limited computational resources
Solution Approach 1:
The patent replaces software-based cryptographic operations with hardware-accelerated homomorphic encryption processing. This substitution provides strong security effectiveness by performing computations on encrypted data through dedicated hardware circuits, while maintaining ease of operation as the system presents a simple encrypted interface to users without requiring them to manage complex cryptographic operations.
4Reliability
If encrypted data processing is implemented, then model and data privacy is improved, but device complexity increases due to the need for specialized encryption schemas and hardware
Solution Approach 1:
The patent merges the encryption and computation functions by integrating homomorphic encryption operations directly into the machine learning model execution pipeline. This merging reduces device complexity by eliminating separate encryption/decryption modules and allowing the model itself to operate on encrypted data, with the encryption schema becoming an intrinsic part of the processing workflow rather than an external add-on.
Data Source
AI summary
A server system that includes a host device initiates an encrypted decision tree model executing on an accelerator coupled with the host device. The encrypted decision tree model encrypted uses an agreed upon encryption schema between the host device and a user device accessing the encrypted decision tree model. The host device receives an input, from the user device, to be evaluated using the encrypted decision tree model. The input is encrypted using the agreed upon encryption schema. The host device using the encrypted decision tree model evaluates the input from the user device without decrypting the input. The accelerator using the encrypted decision tree model generates an encrypted output based on the evaluating. The accelerator device provides the encrypted output to the user device.