InstaHide: Instance-hiding Schemes for Private Distributed Learning (2010.02772v2)

Abstract: How can multiple distributed entities collaboratively train a shared deep net on their private data while preserving privacy? This paper introduces InstaHide, a simple encryption of training images, which can be plugged into existing distributed deep learning pipelines. The encryption is efficient and applying it during training has minor effect on test accuracy. InstaHide encrypts each training image with a "one-time secret key" which consists of mixing a number of randomly chosen images and applying a random pixel-wise mask. Other contributions of this paper include: (a) Using a large public dataset (e.g. ImageNet) for mixing during its encryption, which improves security. (b) Experimental results to show effectiveness in preserving privacy against known attacks with only minor effects on accuracy. (c) Theoretical analysis showing that successfully attacking privacy requires attackers to solve a difficult computational problem. (d) Demonstrating that use of the pixel-wise mask is important for security, since Mixup alone is shown to be insecure to some some efficient attacks. (e) Release of a challenge dataset https://github.com/Hazelsuko07/InstaHide_Challenge Our code is available at https://github.com/Hazelsuko07/InstaHide

• The paper introduces InstaHide, a method that encrypts training images using random mixing and pixel-wise masks to preserve data privacy during distributed learning.
• It details two variants—inside-dataset and cross-dataset—that blend private images with random samples, thereby complicating reconstruction efforts.
• Empirical results on MNIST, CIFAR-10, and ImageNet show that InstaHide maintains high model accuracy with only a minimal drop of 1–4%.

A Critical Overview of InstaHide: Instance-Hiding Schemes for Private Distributed Learning

The paper "InstaHide: Instance-Hiding Schemes for Private Distributed Learning" by Huang et al. addresses critical privacy issues in distributed machine learning. The core motivation is to enable multiple entities to collaboratively train a deep neural network on shared data without compromising individual data privacy. This is crucial in sectors where data sensitivity is paramount, such as healthcare, where legal frameworks like HIPAA and GDPR impose strict compliance standards.

Key Methodology

The paper introduces InstaHide, a method aimed at preserving data privacy through the encryption of training images. InstaHide modifies the distributed learning pipeline by integrating instance-hiding techniques. It employs a "one-time secret key" mechanism, leveraging a combination of randomly selected images and a pixel-wise mask to encrypt each training image. This approach is not only light-weight but maintains efficiency with minimal impact on model accuracy during training.

InstaHide Variants

1. Inside-Dataset InstaHide: This variant encrypts images by mixing them with random images from the same dataset. A pixel-wise random sign-flipping mask is subsequently applied, ensuring that each encryption instance is uniquely key-protected.
2. Cross-Dataset InstaHide: This variant enhances security by also mixing training images with random images from a large public dataset, such as ImageNet. The introduction of public data increases the difficulty of reconstructing original private images.

Theoretical and Experimental Analysis

The authors provide both theoretical insights and empirical results to demonstrate the security and effectiveness of InstaHide. They argue that breaking InstaHide reduces to a computationally challenging problem, analogous to solving a high-dimensional k-SUM problem, which is known for its complexity.

• Security Evaluation: The theoretical analysis suggests that reconstructing the original data requires solving problems that are hard for both classical and modern cryptographic attacks, particularly when the dataset used for mixing is extensive.
• Experimental Validation: Experiments on datasets such as MNIST, CIFAR-10, and ImageNet indicate that InstaHide incurs minimal reductions in test accuracy, only about 1-4% for various configurations when k (the number of mixed images) is set to small values like 4. Compared to Differential Privacy approaches, InstaHide offers better trade-offs between privacy preservation and model accuracy.

Implications and Future Directions

The introduction of InstaHide into distributed learning systems holds several implications for both privacy and utility in machine learning:

• Practical Implications: InstaHide methods can be seamlessly integrated into existing federated learning frameworks, providing an added layer of security without significant overhead.
• Theoretical Implications: The paper opens up new avenues for exploring cryptographic techniques in the field of data privacy for complex learning models. It poses foundational challenges in understanding instance-based security settings and inspires further research into optimizing these techniques for broader data types beyond images.
• Future Directions: The effectiveness of InstaHide, particularly in adversarial settings, suggests unexplored territory in enhancing privacy-preserving machine learning. Future work may involve extending InstaHide to other data forms or adopting adaptive security measures against sophisticated attackers who leverage machine learning techniques for cryptanalysis.

In conclusion, InstaHide represents a promising direction for secure collaborative learning, balancing the needs for data utility and privacy. Its design underlines the importance of cryptographic principles in the growing field of privacy-preserving artificial intelligence.