PyLadiesCon 2024

As artificial intelligence (AI) and machine learning continue to revolutionize industries, the ethical responsibility of safeguarding personal and sensitive data has become a central issue. In fields ranging from healthcare to finance, AI-driven applications are increasingly relying on large datasets, many of which contain sensitive personal information. However, as data usage grows, so do concerns about privacy breaches and misuse. High-profile data breaches and the misuse of personal information have triggered global regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations impose strict requirements on data handling, making it essential for developers and data scientists to integrate privacy-preserving techniques into their workflows to ensure compliance while maintaining trust with users.
In this session, titled "Preserving Privacy in AI: Techniques and Tools for Secure Machine Learning," we will explore how to harness the power of AI while protecting individual privacy. The goal is to equip attendees with both a theoretical understanding and practical tools for implementing privacy-preserving AI techniques. We’ll cover real-world solutions that allow AI systems to extract value from data while minimizing the risk of exposing sensitive information. By addressing the challenge of balancing privacy with innovation, this talk provides actionable insights for developers, data scientists, and AI enthusiasts alike.
At the heart of this discussion will be three key techniques that represent the forefront of privacy-preserving AI: Differential Privacy, Federated Learning, and Homomorphic Encryption. Differential Privacy is a method that adds controlled noise to datasets, ensuring that the contribution of any individual’s data cannot be discerned while still allowing meaningful insights to be drawn. This technique has been successfully adopted by large-scale companies like Apple and Google to anonymize user data in their products while maintaining high standards of privacy.
Federated Learning offers a decentralized approach to machine learning by enabling AI models to be trained on devices without requiring raw data to be transferred to a central server. This technique allows organizations to benefit from insights across distributed data sources, such as smartphones or hospitals, without collecting the actual data itself. It is especially useful for applications like predictive text, where data from users’ devices can be leveraged to improve algorithms without breaching their privacy.
Homomorphic Encryption is another powerful tool that allows computations to be performed on encrypted data, meaning sensitive information never needs to be decrypted during the analysis process. This ensures that even if data is intercepted, it remains unintelligible to attackers. Homomorphic Encryption is particularly valuable in highly regulated industries like finance and healthcare, where sensitive data must remain protected throughout its lifecycle.
In addition to exploring these privacy-preserving techniques, we’ll delve into the open-source Python libraries that enable developers and data scientists to implement these methods in their own workflows.
So in this talk, attendees will learn how privacy-preserving techniques can help comply with regulations like GDPR, CCPA, and HIPAA, while also building AI systems that foster trust and transparency. As AI continues to be integrated into everyday life, users are becoming more aware of how their data is being used. By adopting privacy-first practices, organizations can demonstrate a commitment to ethical AI, helping to build and maintain user trust.
This session is designed to be accessible to a wide audience, whether you’re new to AI or an experienced developer looking to expand your skillset in privacy-preserving techniques.