Binary Neural Network Inference With Garbled Circuits for Data Privacy
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Solution Overview
Problem
Existing deep learning models for remote inference services reveal sensitive user data to cloud servers, violating privacy and facing inefficiencies in cryptographic protocols such as high computational complexity and communication costs.
Innovation Solution
A framework utilizing oblivious binary neural networks (BNNs) with Garbled Circuits (GC) protocol, replacing costly multiplications with XNOR operations, and optimizing network architecture for constant round interactions, ensuring privacy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Homomorphic Encryption or Garbled Circuits are used for secure inference, then privacy preservation is improved, but computational complexity and communication overhead increase significantly
Solution Approach 1:
The patent transforms the neural network parameters from continuous floating-point values to binary values (0 or 1). This parameter change enables the use of simple XOR operations instead of complex multiplications, dramatically reducing computational complexity while maintaining privacy preservation through the Garbled Circuits protocol. The binary weights and activations allow the inference to be performed using only addition and XOR operations, which are computationally efficient in the Garbled Circuits framework.
Solution Approach 2:
The patent extracts and removes the computationally expensive multiplication operations from the inference process by using binary weights. By representing weights as binary values, the patent eliminates the need for complex multiplication operations in the Garbled Circuits protocol, keeping only the necessary XOR and addition operations that are much more efficient to compute.
2Reliability
If Homomorphic Encryption or Garbled Circuits are used for secure inference, then privacy preservation is improved, but communication overhead increases
Solution Approach 1:
By changing the weight parameters to binary values, the patent reduces the amount of data that needs to be encrypted and transmitted. Binary parameters require fewer bits to represent compared to continuous floating-point values, thereby reducing communication overhead while maintaining the security guarantees of the Garbled Circuits protocol.
3Device complexity
If binary neural network is used, then computational complexity is reduced, but inference accuracy may be compromised
Solution Approach 1:
The patent applies partial binarization only to the weight parameters while keeping the input data and activation functions flexible. This partial application of binarization achieves the computational benefits without completely sacrificing accuracy, as the network can still process continuous inputs and use appropriate activation functions to maintain representation power.
Solution Approach 2:
The patent applies different properties to different parts of the network: binary weights for computational efficiency, continuous inputs for flexibility, and specific activation functions for maintaining accuracy. This local differentiation allows each component to optimize for its specific function while working together to achieve both efficiency and accuracy.
4Productivity
If network architecture is optimized through pruning, then productivity is improved, but reliability may be reduced
Solution Approach 1:
The patent performs network pruning and optimization in advance during the training phase, before the actual inference process. By pre-identifying and removing redundant channels and neurons, the network is optimized for fast inference without compromising accuracy, as the pruning decisions are made based on comprehensive training data analysis rather than ad-hoc removal during inference.
Solution Approach 2:
The patent uses feedback from training performance to guide the pruning process. Channels and neurons are pruned based on their contribution to the overall network performance, with less important components being removed first. This feedback-driven approach ensures that accuracy is maintained while achieving productivity gains through a more efficient network architecture.
Data Source
AI summary
A framework is presented that provides a shift in the conceptual and practical realization of privacy-preserving interference on deep neural networks. The framework leverages the concept of the binary neural networks (BNNs) in conjunction with the garbled circuits protocol. In BNNs, the weights and activations are restricted to binary (e.g., ±1) values, substituting the costly multiplications with simple XNOR operations during the inference phase. The XNOR operation is known to be free in the GC protocol; therefore, performing oblivious inference on BNNs using GC results in the removal of costly multiplications. The approach consistent with implementations of the current subject matter provides for oblivious inference on the standard DL benchmarks being performed with minimal, if any, decrease in the prediction accuracy.