Unleashing the Tiger: Inference Attacks on Split Learning (2012.02670v5)

Abstract: We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the present paper, we expose vulnerabilities of the protocol and demonstrate its inherent insecurity by introducing general attack strategies targeting the reconstruction of clients' private training sets. More prominently, we show that a malicious server can actively hijack the learning process of the distributed model and bring it into an insecure state that enables inference attacks on clients' data. We implement different adaptations of the attack and test them on various datasets as well as within realistic threat scenarios. We demonstrate that our attack is able to overcome recently proposed defensive techniques aimed at enhancing the security of the split learning protocol. Finally, we also illustrate the protocol's insecurity against malicious clients by extending previously devised attacks for Federated Learning. To make our results reproducible, we made our code available at https://github.com/pasquini-dario/SplitNN_FSHA.

• The paper introduces the Feature-space Hijacking Attack (FSHA) that manipulates the learning process to reveal private data.
• It demonstrates that existing defenses, such as distance correlation minimization, are ineffective against active adversaries.
• The study underscores the need to re-evaluate privacy-preserving machine learning frameworks in adversarial settings.

Inference Attacks on Split Learning: A Security Analysis

The paper "Unleashing the Tiger: Inference Attacks on Split Learning" by Pasquini, Ateniese, and Bernaschi provides an in-depth security analysis of split learning, a collaborative machine learning framework gaining traction for its resource efficiency and applicability in scenarios where data privacy is paramount. The authors argue that despite its growing adoption, split learning is fundamentally insecure, highlighting significant vulnerabilities that allow for inference attacks capable of reconstructing private training sets.

Key Findings

The crux of the authors' contribution is the identification and demonstration of the "Feature-space Hijacking Attack" (FSHA). This attack leverages the ability of a malicious server to control the learning process, thus forcing the client's models to transition into states susceptible to inference attacks. A key innovation in FSHA is its ability to work across various configurations and datasets, demonstrating its robustness against recently proposed defensive techniques aimed at securing split learning implementations.

1. General Attack Framework: The authors present a general attack strategy whereby a malicious server manipulates the training process to reconstruct the private data by hijacking the feature-space learned by the client's network. Through FSHA, an attacker can actively replace the distributed learning task with one that reveals the private data, essentially converting the network into an autoencoder without the client's awareness.
2. Inadequacy of Existing Defenses: The paper critically evaluates the effectiveness of defense mechanisms, such as distance correlation minimization, and concludes they are insufficient in mitigating FSHA. The key reason is that these defenses assume passive adversaries, while FSHA actively manipulates the learning objective, rendering these defenses ineffective.
3. Vulnerabilities Against Malicious Clients: The paper extends the discourse to consider the threats posed by malicious clients. By adapting inference attacks previously used in federated learning, the authors show that split learning is also vulnerable to attacks from malicious clients aiming to infer components of the model owned by honest participants.

Practical and Theoretical Implications

From a practical standpoint, the paper provides compelling evidence that split learning, in its current form, cannot be considered secure in adversarial environments, particularly those involving a sophisticated adversary capable of real-time manipulation of the learning process. The implications are critical for sectors like healthcare and telecommunications, where the security of training data is a paramount concern.

Theoretically, the findings urge a re-evaluation of assumptions underpinning privacy-preserving machine learning frameworks. The notion that simple architectural modifications or cryptographic techniques can inherently secure distributed learning processes is challenged. This necessitates a paradigm shift towards designing frameworks that can withstand active, as well as passive, adversarial threats.

Future Directions

The paper sets the stage for further exploration into robust defenses against inference attacks in distributed learning paradigms. Possible lines of inquiry include:

• Developing new training protocols that do not allow end-to-end control by any party, hence decentralizing power in the learning process.
• Exploring adversarial robustness techniques that inherently detect and prevent hijacking attempts.
• Designing more sophisticated mechanisms for privacy-preserving aggregations that resist FSHA.

In conclusion, this paper serves as a seminal work in unraveling the vulnerabilities within split learning and has significant implications for the design and deployment of privacy-preserving machine learning frameworks. It highlights the necessity for continued research into more secure and resilient collaborative learning protocols, capable of safeguarding sensitive information in adversarial contexts.