Privacy-Preserving Decision Tree Inference via Homomorphic Encryption
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Solution Overview
Problem
Decision tree-based models are computationally inefficient when implemented using fully homomorphic encryption due to non-linear comparison operations and scaling challenges, making privacy-preserving inferencing difficult.
Innovation Solution
The approach involves balancing and packing the decision tree, using a cryptographic scheme to enable efficient single instruction, multiple data (SIMD) operations, and employing a soft membership computation with an ArgMax function for encrypted data points, facilitating privacy-preserving homomorphic inferencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fully homomorphic encryption is used to process decision tree comparisons, then privacy preservation is improved, but computational efficiency deteriorates
Solution Approach 1:
The decision tree is balanced to create a uniform structure where all leaf nodes are at the same depth. This segmentation allows the encryption scheme to pack multiple comparison operations into a single ciphertext, enabling parallel processing of multiple data points through SIMD operations. The balancing transforms the irregular tree structure into a regular one that is compatible with homomorphic encryption constraints.
Solution Approach 2:
The patent introduces cryptographic packing that maps multiple plaintext values into a single ciphertext vector. This dimensionality change allows batch processing of comparisons where instead of encrypting and processing each comparison individually, multiple comparisons are packed into one encrypted vector operation, significantly improving computational efficiency while maintaining privacy.
2Measurement precision
If non-linear comparison operations are used in decision trees with FHE, then decision accuracy is maintained, but computational complexity increases
Solution Approach 1:
The patent transforms the comparison operation from a non-linear threshold check into a linear operation by modifying how thresholds are represented and applied. Instead of using non-linear activation functions, the implementation uses linear homomorphic addition and multiplication operations on encrypted values, where the comparison result is derived from the sign of the encrypted difference between feature values and thresholds.
Solution Approach 2:
The patent replaces the traditional non-linear comparison mechanism with homomorphic encryption operations. Instead of decrypting values to perform comparisons, the system uses encrypted arithmetic operations (addition, multiplication) that naturally support comparison logic through the mathematical properties of the encryption scheme, eliminating the need for non-linear functions.
3Reliability
If decision trees are processed using FHE without optimization, then privacy is preserved, but operational speed deteriorates
Solution Approach 1:
The decision tree is balanced and preprocessing is performed before the actual inference operations. This preliminary action restructures the tree into an optimized format that enables efficient batch processing. By preparing the tree structure in advance with uniform depth and compatible thresholds, the system eliminates the need for repeated structural adjustments during inference, significantly speeding up operational performance.
Solution Approach 2:
Multiple comparison operations are merged into single batched homomorphic operations. Instead of processing each comparison separately, the patent combines multiple feature comparisons and threshold checks into unified encrypted vector operations. This merging reduces the total number of cryptographic operations required and enables parallel execution, thereby improving operational speed.
Data Source
AI summary
A technique for computationally-efficient privacy-preserving homomorphic inferencing against a decision tree. Inferencing is carried out by a server against encrypted data points provided by a client. Fully homomorphic computation is enabled with respect to the decision tree by intelligently configuring the tree and the real number-valued features that are applied to the tree. To that end, and to the extent the decision tree is unbalanced, the server first balances the tree. A cryptographic packing scheme is then applied to the balanced decision tree and, in particular, to one or more entries in at least one of: an encrypted feature set, and a threshold data set, that are to be used during the decision tree evaluation process. Upon receipt of an encrypted data point, homomorphic inferencing on the configured decision tree is performed using a highly-accurate approximation comparator, which implements a “soft” membership recursive computation on real numbers, all in an oblivious manner.