Low Latency Privacy Preserving Inference (1812.10659v2)

Abstract: When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the width and depth of neural networks that can be used (and hence the accuracy) and exhibit high latency even for relatively simple networks. In this study we provide two solutions that address these limitations. In the first solution, we present more than $10\times$ improvement in latency and enable inference on wider networks compared to prior attempts with the same level of security. The improved performance is achieved by novel methods to represent the data during the computation. In the second solution, we apply the method of transfer learning to provide private inference services using deep networks with latency of $\sim0.16$ seconds. We demonstrate the efficacy of our methods on several computer vision tasks.

• The paper introduces LoLa, which reduces inference latency by over tenfold through novel encrypted data representations that optimize neural network layer computations.
• The paper demonstrates a transfer learning approach that pre-processes deep features before encryption, achieving prediction times as low as 0.16 seconds.
• The method efficiently balances accuracy, security, and memory usage, significantly lowering the computational demands for privacy-preserving AI applications.

Overview of "Low Latency Privacy Preserving Inference"

In the paper presented by Brutzkus, Elisha, and Gilad-Bachrach, the authors address significant challenges in machine learning applications involving sensitive data by presenting methods for low latency, privacy-preserving inference. Recognizing the balance needed among accuracy, security, and computational complexity, they offer solutions leveraging Homomorphic Encryption (HE) to secure neural network predictions without compromising data privacy.

Key Contributions

The paper provides two pivotal contributions to enhance private neural network inference:

1. Low Latency CryptoNets (LoLa): This approach revolutionizes how predictions are handled by reducing latency more than tenfold compared to previous methods like CryptoNets. By reimagining data representation during computation, LoLa improves efficiency and allows the use of wider and more complex networks. Utilizing alternative representations of encrypted messages, LoLa reduces memory usage significantly and facilitates inferences on broader networks.
2. Transfer Learning for Deep Networks: The second solution employs transfer learning techniques to manage predictions on deeper networks, achieving latency as low as 0.16 seconds. Here, deep features from raw data are extracted before encryption and used for private evaluation, making the previously intractable task of deep network inference feasible under privacy constraints.

Methodological Insights

The paper systematically outlines how HE—particularly the Brakerski/Fan-Vercauteren (BFV) approach—enables operations on encrypted data. Although HE offers robust security, it comes with limitations on the operations it allows. LoLa introduces novel methods to overcome these restrictions by alternating between various representations such as dense, sparse, interleaved, and convolution representations throughout network layers. Each representation is strategically chosen to optimize computation depending on the layer type—convolutional versus fully connected—and operation—e.g., matrix-vector multiplications.

For instance, while CryptoNets represented each node separately leading to high latency and memory bottlenecks, LoLa encodes entire layers, dramatically reducing computational demands while retaining accuracy. This strategic use of multiple representations illustrates an innovative method for mitigating the inherent limitations of HE.

Experimental Validation

The authors demonstrate the efficacy of their methods through experiments on well-known datasets like MNIST and CIFAR-10. LoLa reduces the MNIST task's prediction time from 205 seconds to just 2.2 seconds, and tackles inference on CIFAR-10—a considerably more complex dataset—using only a fraction of the memory previously required. The transfer learning approach is evaluated on the CalTech-101 dataset, showing that preprocessed, encrypted data can achieve accurate predictions quickly, thus suggesting its potential for real-world applications where computational resources or latency considerations are critical.

Implications and Future Work

This paper has considerable implications for privacy-preserving AI applications across security-sensitive fields such as healthcare, finance, and image processing. By mitigating memory and computational bottlenecks, it paves the way for more efficient deployment of secure AI systems in environments where data privacy is paramount.

Future developments could explore optimizing these methods further through automated selection of representations or incorporating advancements in encryption schemes. Additionally, extending these techniques to training tasks or deploying them in conjunction with other cryptographic protocols could enhance their applicability and performance.

Overall, Brutzkus and colleagues provide a comprehensive examination and breakthrough solutions that elevate the feasibility of privacy-preserving machine learning inference, pushing the boundaries of what is possible when it comes to computation on encrypted data.