Machine learning (ML) models have become increasingly popular, but with this popularity comes growing concern about information leakage about training data. Research has shown that an adversary can infer sensitive information from ML models through various methods, such as observing model parameters or results. To address this issue, researchers have started using privacy games to capture threat models and understand the risks of deploying ML models.
The state of the art in understanding and mitigating information leakage on training data in machine learning (ML) models involves the use of privacy games to capture threat models and measure the risks of implementing ML models. Research in this area is still in its infancy, with no well-established standards for game-based definitions and a lack of understanding of the relationships between different privacy games. However, there is growing interest in this area, with researchers working to establish relationships between privacy risks and developing ways to mitigate these risks. Recently, a research team from Microsoft and the University of Virginia published an article that aims to summarize this growing concern and the research being done to understand and mitigate information leakage about training data in ML models.
The article presents the first systematization of knowledge about the risks of privacy inference in ML. It proposes a unified representation of five fundamental privacy risks as games: membership inference, attribute inference, ownership inference, differential privacy distinguishability, and data reconstruction. Furthermore, the article establishes and rigorously tests the relationships between the above risks and presents a case study showing that a scenario described as a variant of membership inference in the literature can be decomposed into a combination of membership and ownership inference. The authors discuss strategies for choosing privacy games, their current and future uses, and their limitations. Furthermore, they suggest that gamers should take advantage of the building blocks provided in the article to design games that accurately capture the specific threat models of their application.
The article also states that the use of privacy games has become predominant in the machine learning privacy literature and has been used to support the empirical evaluation of machine learning systems against various threats and to compare the strength of the properties. of privacy and attacks. It is mentioned that in the future, privacy games can be used to communicate privacy properties, making explicit the threat model and all assumptions about creating and training datasets, and can facilitate discussion of the goals and privacy guarantees with stakeholders who make ML privacy guidelines and decisions. Furthermore, the game-based formalism can be used to reason about games using program logic and manipulate them using program transformations. The article also highlights the limitations of privacy games, such as the fact that they can be complex and sometimes require reasoning about ongoing distributions.
In conclusion, understanding and mitigating information leakage about training data in machine learning (ML) models is a growing concern. This article has provided an overview of this concern and the research being done to understand and mitigate information leakage about training data in ML models. It has also provided strategies for choosing privacy games, their current and future uses, and their limitations. Privacy games have been used to capture threat models and measure the risks of implementing ML models. Game users have been advised to take advantage of the building blocks provided in the article to design games that accurately capture their application-specific threat models. Additionally, in the future, privacy games may be used to communicate privacy properties and facilitate discussion of privacy goals and guarantees with stakeholders making ML privacy guidelines and decisions.
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Mahmoud is a PhD researcher in machine learning. He also has a
bachelor’s degree in physical sciences and master’s degree in
telecommunication systems and networks. Your current areas of
the research concerns computer vision, stock market prediction and
learning. He produced several scientific articles on the relationship with the person.
identification and study of the robustness and stability of depths
networks
