FLiP: Using Dataset Distillation to Achieve Principle of Least Privilege in Federated Learning for Privacy Preservation

Основні поняття

FLiP enhances privacy in Federated Learning by employing local-global dataset distillation, adhering to the Principle of Least Privilege, which minimizes shared information to only what's essential for model training, thereby mitigating privacy risks.

Анотація

• Bibliographic Information: Xu, S., Ke, X., Li, S., Su, X., wu, H., Xu, F., & Zhong, S. (2024). FLiP: Privacy-Preserving Federated Learning based on the Principle of Least Privileg. arXiv preprint arXiv:2410.19548v1.
• Research Objective: This paper introduces FLiP, a novel Federated Learning framework designed to enhance data privacy by implementing the Principle of Least Privilege (PoLP) through a local-global dataset distillation mechanism.
• Methodology: FLiP locally extracts task-relevant information from raw data, generating a distilled dataset significantly smaller than the original. These distilled datasets are then aggregated and shared among clients, enabling collaborative model training while minimizing the risk of exposing sensitive information. The authors evaluate FLiP's privacy preservation using task-irrelevant attribute inference attacks and membership inference attacks.
• Key Findings: FLiP achieves comparable accuracy to vanilla Federated Learning across various datasets (MNIST, CIFAR-10, CIFAR-100) and model architectures (TinyResNet, AlexNet, ConvNet). The system demonstrates robustness against task-irrelevant attribute inference attacks, achieving near-random guess accuracy in most cases. Additionally, FLiP effectively mitigates membership inference attacks, significantly reducing the accuracy of determining if a specific sample was used in training.
• Main Conclusions: FLiP successfully integrates the PoLP into Federated Learning, striking a balance between model accuracy and privacy protection. The local-global dataset distillation effectively minimizes shared information, reducing the risk of privacy breaches while maintaining model performance.
• Significance: This research contributes significantly to the field of privacy-preserving machine learning, offering a practical solution for secure collaborative learning in scenarios where data privacy is paramount.
• Limitations and Future Research: The authors acknowledge that the effectiveness of FLiP's privacy preservation is influenced by the number of distilled samples used. Future research could explore adaptive mechanisms to dynamically adjust this number based on the sensitivity of the data and the desired level of privacy. Further investigation into the robustness of FLiP against other sophisticated privacy attacks is also warranted.

Статистика

For every 5 additional distilled samples per category, the accuracy increases by an average of 0.12%, 2.172%, and 3.985% on MNIST, CIFAR-10, and CIFAR-100, respectively.
7 out of 12 task-irrelevant attribute inference attacks resulted in an accuracy of 0.5 or less, indicating effective defense against such attacks.
In membership inference attacks, FLiP achieved an attack accuracy of 49.75% compared to 59.14% for vanilla Federated Learning, demonstrating significant resistance against such attacks.

Цитати

Ключові висновки, отримані з

FLiP: Privacy-Preserving Federated Learning based on the Principle of Least Privileg

by ShiMao Xu, X... о arxiv.org 10-28-2024

https://arxiv.org/pdf/2410.19548.pdf

Глибші Запити

How can FLiP be adapted to handle scenarios with heterogeneous data distributions across clients, where the definition of "task-relevant" information might vary?

Addressing heterogeneous data distributions in Federated Learning (FL) while upholding the Principle of Least Privilege (PoLP) presents a significant challenge. Here's a breakdown of potential adaptations for FLiP:
Challenges:

Varying Task-Relevance: The paper assumes clients share the same categories of samples. In heterogeneous settings, what's "task-relevant" for one client might be irrelevant or even sensitive for another.
Global Distillation Bias: Directly aggregating distilled data from diverse distributions could bias the global view towards clients with larger or more informative datasets, potentially leaking information about the under-represented distributions.
Potential Adaptations:

Personalized/Clustered Distillation:

Instead of a single global distillation process, implement personalized or clustered approaches.
Personalized: Each client distills data based on its local task objectives, potentially sharing only a subset of distilled samples relevant to the global model.
Clustered: Clients with similar data distributions (identified through techniques like federated clustering) could form clusters, performing distillation within the cluster and sharing a representative global view.

Federated Task Relevance Determination:

Develop mechanisms for clients to collaboratively define "task-relevance" in a privacy-preserving manner.
Secure Multi-Party Computation (SMPC): Clients could jointly compute a global task relevance metric without revealing their individual data distributions.
Federated Feature Selection: Explore techniques where clients collaboratively select a subset of features most relevant to the global task, guiding the distillation process.

Differential Privacy Augmentation:

Introduce Differential Privacy (DP) mechanisms during the global aggregation of distilled data to add noise and mask client-specific information.
Local DP: Clients add noise to their distilled data before sharing, providing stronger privacy but potentially impacting accuracy.
DP-SGD: Apply DP during the model training process on the aggregated distilled data to protect against inferences from model updates.

Robust Aggregation Schemes:

Employ aggregation methods less sensitive to outliers or biases introduced by heterogeneous data.
Median/Trimmed Mean: Instead of averaging distilled data, use more robust aggregators that reduce the influence of potentially outlier clients.

Key Considerations:

Trade-offs: Balancing privacy, accuracy, and communication costs will be crucial. More complex adaptations might introduce higher communication overhead.
Scalability: Solutions should be scalable to a large number of clients and handle dynamic changes in data distributions over time.

Could the privacy benefits of FLiP be further enhanced by incorporating other privacy-enhancing techniques, such as differential privacy, alongside dataset distillation?

Yes, absolutely. Combining FLiP's dataset distillation with Differential Privacy (DP) mechanisms can significantly enhance privacy in Federated Learning. Here's how:
Synergy of Techniques:

FLiP (Dataset Distillation): Reduces the amount of raw data shared, focusing on task-relevant information. This limits the exposure of sensitive attributes not directly related to the task.
Differential Privacy (DP): Provides a formal privacy guarantee by adding carefully calibrated noise, making it difficult for adversaries to infer individual data points from the shared information.
Incorporation Strategies:

Local Differential Privacy (LDP) on Distilled Data:

Mechanism: Each client applies LDP to their distilled samples (e.g., adding noise to pixel values in image data) before sending them to the server.
Benefit: Stronger privacy protection as even the server doesn't have access to the true distilled data.
Trade-off: Higher privacy comes at the cost of potential accuracy loss due to the added noise.

Differential Privacy during Global Aggregation:

Mechanism: The server applies DP mechanisms (e.g., Gaussian Mechanism, Laplacian Mechanism) while aggregating the distilled data received from clients.
Benefit: Protects against inferences from the aggregated global view, making it harder to distinguish contributions from individual clients.
Trade-off: Moderate privacy gain with a potentially smaller impact on accuracy compared to LDP.

DP-SGD on Model Training:

Mechanism: Apply DP during the Stochastic Gradient Descent (SGD) process while training the global model on the aggregated distilled data.
Benefit: Protects against attacks that aim to infer information from the model updates during training.
Trade-off: Can impact model convergence and accuracy, requiring careful tuning of DP parameters.

Advantages of Combined Approach:

Layered Privacy: Provides multiple layers of protection, making it harder for adversaries to compromise privacy.
Formal Guarantees: DP offers provable privacy guarantees, quantifying the level of privacy risk.
Flexibility: Different DP mechanisms can be tailored to specific privacy requirements and data characteristics.
Challenges:

Parameter Tuning: Finding the optimal balance between privacy (controlled by DP parameters) and accuracy can be challenging.
Communication Overhead: LDP might increase communication costs as clients send larger noisy data.

What are the potential implications of applying the Principle of Least Privilege in other domains beyond Federated Learning, such as data sharing in healthcare or finance?

The Principle of Least Privilege (PoLP) has profound implications for data sharing in privacy-sensitive domains like healthcare and finance, extending far beyond Federated Learning.
Healthcare:

Electronic Health Records (EHRs):

Challenge: EHRs contain highly sensitive patient data. Sharing them for research or treatment coordination poses significant privacy risks.
PoLP Solution: Granting access to only the specific data elements necessary for a particular task (e.g., a doctor only sees relevant medical history, a researcher analyzing a specific condition gets access to a subset of data).

Genomic Data Sharing:

Challenge: Genomic data is extremely sensitive and can reveal predispositions to diseases.
PoLP Solution: Allowing researchers to query and analyze aggregated genomic data without accessing individual-level information. Techniques like homomorphic encryption can enable computations on encrypted data without decryption.

Medical Imaging:

Challenge: Medical images contain identifiable information.
PoLP Solution: Sharing de-identified images or using federated learning to train models on decentralized data without directly sharing the images.
Finance:

Fraud Detection:

Challenge: Sharing transaction data for fraud detection can expose sensitive financial information.
PoLP Solution: Using techniques like secure multi-party computation (SMPC) to allow institutions to collaboratively detect fraudulent activities without revealing their individual customer data.

Credit Scoring:

Challenge: Credit scoring models rely on vast amounts of personal financial data.
PoLP Solution: Developing privacy-preserving credit scoring systems that use minimal data attributes or leverage techniques like homomorphic encryption to protect sensitive information during computation.

Anti-Money Laundering (AML):

Challenge: AML efforts require sharing information about suspicious transactions.
PoLP Solution: Enabling secure and privacy-preserving information sharing between financial institutions using techniques like differential privacy or secure enclaves.
Broader Implications:

Increased Trust: PoLP fosters trust between data holders and data users by ensuring responsible data handling.
Ethical Data Governance: It promotes ethical data practices and aligns with data minimization principles outlined in regulations like GDPR.
Innovation: By enabling secure and controlled data sharing, PoLP can unlock new opportunities for research, collaboration, and the development of privacy-enhancing technologies.
Challenges:

Granular Access Control: Implementing fine-grained access control mechanisms for complex data structures can be technically challenging.
Dynamic Contexts: Defining and enforcing PoLP in dynamic situations where data usage requirements change over time requires flexible solutions.
Awareness and Adoption: Promoting awareness and encouraging the adoption of PoLP principles across different sectors is crucial for its widespread impact.