insight - Computer Security and Privacy - # Homomorphic Encryption Pruning

HE-PEx: A Framework for Efficiently Pruning Machine Learning Models Under Homomorphic Encryption

Q: How can HE-PEx be adapted for other emerging privacy-enhancing technologies beyond homomorphic encryption, such as secure multi-party computation or federated learning?

HE-PEx, at its core, addresses the challenge of reducing the computational and communication overhead associated with privacy-preserving machine learning by maximizing tile sparsity. This principle can be extended to other privacy-enhancing technologies beyond homomorphic encryption: Secure Multi-party Computation (MPC): Data Partitioning and Sparse Model Sharing: In MPC, data is split among multiple parties. HE-PEx's permutation techniques could be adapted to strategically partition the model weights or activations such that the communication of zero tiles is minimized during the computation. This reduces communication rounds and improves efficiency. Protocol-Specific Optimizations: Different MPC protocols (e.g., secret sharing, garbled circuits) have unique communication patterns. HE-PEx's principles could inspire the design of pruning and packing strategies tailored to these protocols, minimizing the transmission of unnecessary data. Federated Learning (FL): Bandwidth-Efficient Model Updates: In FL, clients train local models and share updates. HE-PEx's focus on tile sparsity can be applied to transmit only the essential model updates, significantly reducing communication costs, a major bottleneck in FL. Sparsity-Aware Aggregation: The server aggregating model updates in FL can leverage the knowledge of sparse structures to design efficient aggregation algorithms. This reduces computation and potentially improves the convergence rate of the global model. Key Considerations for Adaptation: Security Model: HE-PEx assumes a semi-honest threat model. Adaptations to MPC or FL need to carefully consider the specific security guarantees of the chosen protocol and potential vulnerabilities introduced by sparsity. Data Distribution: The effectiveness of HE-PEx's permutation relies on the underlying data distribution. Adaptations should account for data heterogeneity in FL or the partitioned nature of data in MPC.

Q: Could the increased sparsity in the pruned model potentially introduce new vulnerabilities to side-channel attacks, and if so, how can these risks be mitigated?

Yes, the increased sparsity introduced by HE-PEx could potentially exacerbate vulnerabilities to side-channel attacks, particularly in scenarios where the adversary has some level of access to the system during inference: Potential Vulnerabilities: Timing Attacks: Sparse models might exhibit variations in inference time depending on the activation patterns, potentially leaking information about the input data or the model's structure. Cache Attacks: Sparse data structures can lead to characteristic cache access patterns, which a privileged adversary might exploit to infer information about the data being processed. Fault Attacks: Introducing faults during the computation on a sparse model might produce more predictable errors, potentially revealing sensitive information. Mitigation Strategies: Inference Time Regularization: Ensure that the inference time remains constant regardless of the input data or the activation patterns. This can be achieved by adding dummy operations or padding. Cache-Resistant Data Structures and Algorithms: Employ data structures and algorithms designed to minimize cache-based information leakage. Techniques like oblivious RAM (ORAM) can be explored. Blinding Techniques: Introduce random noise or masking to obfuscate the relationship between the sparsity patterns and the sensitive data. Differential Privacy: Adding carefully calibrated noise to the model's outputs can provide formal privacy guarantees against side-channel attacks that aim to infer sensitive information from the output. Importance of Threat Modeling: A thorough threat model is crucial to assess the specific side-channel risks in the deployment environment and guide the selection of appropriate mitigation strategies.

Q: If we view the process of pruning as finding a simpler model that approximates the original, what does this imply about the inherent complexity of tasks that we aim to solve with machine learning?

Viewing pruning as finding a simpler model that approximates the original suggests that: Overparameterization is Common: The fact that we can often significantly prune a large model without a substantial loss in performance implies that many machine learning models are initially overparameterized. They contain more parameters than are strictly necessary to solve the task effectively. Task Complexity is Often Lower: The success of pruning suggests that the inherent complexity of many tasks we tackle with machine learning might be lower than the size of the models we use would suggest. Simpler models can often capture the underlying patterns in the data sufficiently well. Potential for Efficiency Gains: This observation highlights the potential for developing more efficient machine learning pipelines. By focusing on finding the right level of model complexity for a given task, we can reduce computational costs, memory requirements, and potentially improve generalization performance. Implications for Future Research: Understanding Model Complexity: Further research is needed to understand the relationship between model complexity, task complexity, and generalization performance. This can guide the development of more efficient model architectures and training algorithms. Pruning as a Tool for Insight: Pruning can be used not just for model compression but also as a tool for gaining insights into the data and the learning process. Analyzing the pruned models can reveal which features or interactions are most important for a given task. Beyond Pruning: Exploring other techniques for finding simpler, more efficient representations of complex functions, such as model distillation or architecture search, can lead to more sustainable and scalable machine learning solutions.

Core Concepts

HE-PEx is a novel framework that improves the efficiency of privacy-preserving machine learning inference using homomorphic encryption by introducing new pruning methods that significantly reduce the latency and memory requirements while preserving data privacy.

Abstract

Bibliographic Information

Aharoni, E., Baruch, M., Bose, P., Buyuktosunoglu, A., Drucker, N., Pal, S., Pelleg, T., Sarpatwar, K., Shaul, H., Soceanu, O., & Vaculin, R. (2024). Efficient Pruning for Machine Learning under Homomorphic Encryption. In ESORICS 2023: European Symposium on Research in Computer Security (pp. 11-11). Springer. https://doi.org/10.1007/978-3-031-51482-1_11

Research Objective

This research paper aims to address the challenge of high latency and memory requirements associated with homomorphic encryption (HE) in privacy-preserving machine learning (PPML) by introducing a novel pruning framework called HE-PEx.

Methodology

The researchers developed HE-PEx, a framework that combines four main primitives: prune, permute, pack, and expand. They implemented various pruning schemes based on these primitives, including a novel co-permutation algorithm to enhance tile sparsity without compromising accuracy. The team evaluated HE-PEx on four PPML networks (MLPs, CNNs, and autoencoders) trained on MNIST, CIFAR-10, SVHN, and COVIDx CT-2A datasets. They compared their methods with existing techniques, including an adaptation of the Hunter scheme, using metrics like tile sparsity, inference accuracy/loss, latency, and memory requirements.

Key Findings

HE-PEx significantly reduces the computational overhead of PPML inference under HE. The proposed techniques achieved tile sparsities of up to 95% (average 61%) across different datasets and network architectures, with a minimal degradation in inference accuracy/loss (within 2.5%). Compared to the state-of-the-art pruning technique, HE-PEx generated networks with 70% fewer ciphertexts on average for the same degradation limit. This sparsity translated to a 10–35% improvement in inference speed and a 17–35% reduction in memory requirements compared to unpruned models in a privacy-preserving image denoising application.

Main Conclusions

HE-PEx offers a practical solution for deploying efficient and privacy-preserving machine learning models using HE. The framework's ability to significantly reduce computational overhead without compromising accuracy makes it a valuable tool for real-world PPML applications.

Significance

This research significantly contributes to the field of PPML by addressing a major obstacle to the wider adoption of HE: its computational inefficiency. HE-PEx opens up new possibilities for deploying complex machine learning models in privacy-sensitive domains like healthcare and finance.

Limitations and Future Research

While HE-PEx demonstrates significant improvements, the authors acknowledge that exploring different pruning thresholds for specific layers could further enhance performance. Future research could investigate the application of hyperparameter search techniques to optimize these thresholds. Additionally, further investigation into the privacy implications of pruned models in HE would be beneficial.

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Stats

Pruning 85% of NN weights may only lead to the elimination of 17% of the ciphertexts.
HE-PEx achieves tile sparsities of up to 95% (average 61%) across datasets and NNs.
HE-PEx improves inference speed by 10–35% and reduces memory requirements by 17–35% compared to unpruned models.
HE-PEx generates networks with 70% fewer ciphertexts on average compared to the state-of-the-art technique.

Quotes

"Naïve application of [plaintext pruning] fails due to the fundamental reason that while pruning may introduce zeros, if the zeros lie in a ciphertext with even one other non-zero, then the ciphertext cannot be eliminated."
"Our techniques produce networks with tile sparsities of up to 95% (average 61%) across the datasets and NNs, within a limit of 2.5% degradation in network accuracy/loss. These improve upon the SotA by an average of 70%."

Key Insights Distilled From

Efficient Pruning for Machine Learning Under Homomorphic Encryption

by Ehud Aharoni... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2207.03384.pdf

Efficient Pruning for Machine Learning Under Homomorphic Encryption

Deeper Inquiries

How can HE-PEx be adapted for other emerging privacy-enhancing technologies beyond homomorphic encryption, such as secure multi-party computation or federated learning?

HE-PEx, at its core, addresses the challenge of reducing the computational and communication overhead associated with privacy-preserving machine learning by maximizing tile sparsity. This principle can be extended to other privacy-enhancing technologies beyond homomorphic encryption:
Secure Multi-party Computation (MPC):

Data Partitioning and Sparse Model Sharing: In MPC, data is split among multiple parties. HE-PEx's permutation techniques could be adapted to strategically partition the model weights or activations such that the communication of zero tiles is minimized during the computation. This reduces communication rounds and improves efficiency.
Protocol-Specific Optimizations:  Different MPC protocols (e.g., secret sharing, garbled circuits) have unique communication patterns. HE-PEx's principles could inspire the design of pruning and packing strategies tailored to these protocols, minimizing the transmission of unnecessary data.
Federated Learning (FL):

Bandwidth-Efficient Model Updates: In FL, clients train local models and share updates. HE-PEx's focus on tile sparsity can be applied to transmit only the essential model updates, significantly reducing communication costs, a major bottleneck in FL.
Sparsity-Aware Aggregation:  The server aggregating model updates in FL can leverage the knowledge of sparse structures to design efficient aggregation algorithms. This reduces computation and potentially improves the convergence rate of the global model.
Key Considerations for Adaptation:

Security Model:  HE-PEx assumes a semi-honest threat model. Adaptations to MPC or FL need to carefully consider the specific security guarantees of the chosen protocol and potential vulnerabilities introduced by sparsity.
Data Distribution: The effectiveness of HE-PEx's permutation relies on the underlying data distribution. Adaptations should account for data heterogeneity in FL or the partitioned nature of data in MPC.

Could the increased sparsity in the pruned model potentially introduce new vulnerabilities to side-channel attacks, and if so, how can these risks be mitigated?

Yes, the increased sparsity introduced by HE-PEx could potentially exacerbate vulnerabilities to side-channel attacks, particularly in scenarios where the adversary has some level of access to the system during inference:
Potential Vulnerabilities:

Timing Attacks:  Sparse models might exhibit variations in inference time depending on the activation patterns, potentially leaking information about the input data or the model's structure.
Cache Attacks:  Sparse data structures can lead to characteristic cache access patterns, which a privileged adversary might exploit to infer information about the data being processed.
Fault Attacks:  Introducing faults during the computation on a sparse model might produce more predictable errors, potentially revealing sensitive information.
Mitigation Strategies:

Inference Time Regularization:  Ensure that the inference time remains constant regardless of the input data or the activation patterns. This can be achieved by adding dummy operations or padding.
Cache-Resistant Data Structures and Algorithms: Employ data structures and algorithms designed to minimize cache-based information leakage. Techniques like oblivious RAM (ORAM) can be explored.
Blinding Techniques:  Introduce random noise or masking to obfuscate the relationship between the sparsity patterns and the sensitive data.
Differential Privacy:  Adding carefully calibrated noise to the model's outputs can provide formal privacy guarantees against side-channel attacks that aim to infer sensitive information from the output.
Importance of Threat Modeling:
A thorough threat model is crucial to assess the specific side-channel risks in the deployment environment and guide the selection of appropriate mitigation strategies.

If we view the process of pruning as finding a simpler model that approximates the original, what does this imply about the inherent complexity of tasks that we aim to solve with machine learning?

Viewing pruning as finding a simpler model that approximates the original suggests that:

Overparameterization is Common: The fact that we can often significantly prune a large model without a substantial loss in performance implies that many machine learning models are initially overparameterized. They contain more parameters than are strictly necessary to solve the task effectively.
Task Complexity is Often Lower: The success of pruning suggests that the inherent complexity of many tasks we tackle with machine learning might be lower than the size of the models we use would suggest. Simpler models can often capture the underlying patterns in the data sufficiently well.
Potential for Efficiency Gains: This observation highlights the potential for developing more efficient machine learning pipelines. By focusing on finding the right level of model complexity for a given task, we can reduce computational costs, memory requirements, and potentially improve generalization performance.
Implications for Future Research:

Understanding Model Complexity:  Further research is needed to understand the relationship between model complexity, task complexity, and generalization performance. This can guide the development of more efficient model architectures and training algorithms.
Pruning as a Tool for Insight:  Pruning can be used not just for model compression but also as a tool for gaining insights into the data and the learning process. Analyzing the pruned models can reveal which features or interactions are most important for a given task.
Beyond Pruning:  Exploring other techniques for finding simpler, more efficient representations of complex functions, such as model distillation or architecture search, can lead to more sustainable and scalable machine learning solutions.