Membership Information Leakage in Distributed Edge Intelligence Systems: Risks and Mitigation Strategies

Core Concepts

Distributed edge intelligence systems are vulnerable to membership inference attacks that can lead to sensitive data leakage. Effective defense mechanisms are necessary to safeguard data privacy in these systems.

Abstract

The paper investigates security threats within distributed edge intelligence systems, focusing on membership inference attacks (MIA) to elucidate potential data leakage. It explores various MIA techniques, including NN-based attacks, Metric-based attacks, and Differential attacks, and evaluates their performance on diverse participant client users.
The key findings are:

NN-based attacks can achieve high attack performance (>82%) across varying numbers of clients, but the effectiveness decreases as the system size increases.
Metric-based attacks, such as those based on prediction confidence, can be more effective than those based on prediction entropy in the distributed edge setting.
Differential attacks, especially under non-IID conditions, can achieve high accuracy (>80%) in detecting membership information leakage.
Defense mechanisms like Regularization and Dropout can help mitigate the privacy risks, with Dropout proving more effective than Regularization.
The paper contributes to safeguarding data privacy in the context of distributed edge intelligence systems by identifying vulnerabilities and proposing effective defense strategies.

Stats

Distributed edge intelligence systems with 2-5 clients were evaluated.
CIFAR-10, CIFAR-100, and News datasets were used in the experiments.
NN-based attacks achieved up to 83% accuracy on the CIFAR-10 dataset.
Metric-based attacks based on prediction confidence achieved up to 66% accuracy on the distributed edge system with 4 clients.
Differential attacks achieved up to 80% accuracy under non-IID conditions on the CIFAR-100 dataset.

Quotes

"Experimental findings validate the efficacy of our approach in detecting data leakage issues within edge intelligence systems, while also highlighting the utility of our defense mechanisms in mitigating this security threat."
"Regularization does not yield significant defense results. However, it appears that the differential attack itself may have a better defense against regularization, as evidenced by the improved defense results obtained when employing conventional attacks."

Key Insights Distilled From

EdgeLeakage: Membership Information Leakage in Distributed Edge Intelligence Systems

by Kong... at arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.16851.pdf

Deeper Inquiries

What other defense strategies, beyond Regularization and Dropout, could be explored to further enhance the privacy protection in distributed edge intelligence systems

To further enhance privacy protection in distributed edge intelligence systems, additional defense strategies can be explored. One approach is Differential Privacy, which adds noise to the training data or model parameters to prevent attackers from inferring sensitive information. By introducing controlled amounts of noise, the privacy of individual data points is preserved while still allowing for effective model training. Another strategy is Homomorphic Encryption, which enables computations on encrypted data without decrypting it, thus protecting the privacy of the data throughout the computation process. Secure Multi-Party Computation (MPC) is another technique that allows multiple parties to jointly compute a function over their inputs without revealing the inputs to each other. By leveraging MPC, sensitive data can be processed collaboratively while maintaining privacy. Additionally, Secure Enclave technologies can be utilized to create secure and isolated environments for processing sensitive data, ensuring that data remains protected even during computation.

How can the proposed defense mechanisms be extended to address other types of attacks, such as model inversion or property inference attacks, in the context of distributed edge intelligence

The defense mechanisms proposed in the context of membership inference attacks can be extended to address other types of attacks, such as model inversion or property inference attacks, in distributed edge intelligence systems. For model inversion attacks, techniques like input perturbation can be employed to distort the input data in a way that prevents attackers from reconstructing sensitive information about the training data. Additionally, Generative Adversarial Networks (GANs) can be used to generate synthetic data that resembles the training data but does not reveal sensitive information. To mitigate property inference attacks, differential privacy techniques can be applied to add noise to the model outputs, making it harder for attackers to infer specific properties of the training data. Regularization methods can also be adapted to enforce constraints on the model to prevent leakage of sensitive information.

What are the potential implications of membership information leakage in real-world applications of distributed edge intelligence systems, and how can the findings of this study inform the development of privacy-preserving solutions in those domains

Membership information leakage in real-world applications of distributed edge intelligence systems can have significant implications for data privacy and security. In sectors like healthcare, finance, and IoT, where sensitive data is processed at the edge, the exposure of membership information can lead to unauthorized access, data breaches, and privacy violations. For example, in healthcare, the leakage of patient data membership can compromise patient confidentiality and trust in the healthcare system. Similarly, in financial services, the exposure of client information can result in financial fraud and identity theft. By addressing membership inference leakage through the findings of this study, privacy-preserving solutions can be developed to safeguard sensitive data in these domains. Implementing robust encryption techniques, access control mechanisms, and secure computation protocols can help mitigate the risks associated with membership information leakage and ensure the confidentiality of data in distributed edge intelligence systems.