Unraveling Links Between ML Generalization, Privacy, and Inference Attacks: New Formalism and Insights Revealed
Formalism to Study the Interplay between Generalization and Inference Attacks in ML Models
As machine learning (ML) algorithms continue to be integrated into various applications, privacy and security concerns also rise. Recent research underscores the vulnerabilities of ML models to inference attacks, which undermine the privacy of individuals and organizations. A novel formalism has been proposed to study these attacks, connecting their potential to generalization and memorization in ML models.
Previous research in the field has primarily focused on data-dependent attack strategies. However, these studies have limitations in understanding and addressing the connections between generalization, memorization, and privacy attacks. In light of these limitations, a new approach is introduced that aims to bridge these gaps and offer valuable insights.
The formalism proposed in this research seeks to study the interplay between generalization, Differential Privacy (DP), attribute, and membership inference attacks. The framework does not make assumptions on the distribution of model parameters given the training data, thus providing a generalized approach to address privacy concerns.
Notably, the study extends results to the general case of tail-bounded loss functions. This research focuses on a Bayesian attacker with white-box access, offering an upper bound on the probability of success in launching an attack. It further debunks the statement that “generalization implies privacy,” elucidating a more complex relationship between the two.
The formalism introduced models membership and attribute inference attacks on ML systems. It offers a flexible framework that can be applied to various problem setups, helping to address the range of privacy concerns present in many ML applications.
The establishment of universal bounds on the success rate of inference attacks is noteworthy in this research. These bounds serve as a privacy guarantee and can inform the design of privacy defense mechanisms for ML models, fostering more secure applications moving forward.
The article also delves into the relationship between the generalization gap and membership inference. The authors demonstrate that poor generalization can lead to significant privacy leakage. Additionally, the role of information stored by trained models in privacy attacks is scrutinized, further illuminating these connections.
Experiments carried out in linear regression and deep neural networks for classification provide valuable insights on information leakage in ML models. Employing bounds to assess the success rate of attackers showcases the utility of this approach. Furthermore, lower bounds play a crucial role in establishing the presence of information leakage.
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