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Machine Learning Privacy: Protecting Data in the AI Era
- Machine learning models require large amounts of data, including personal information like health records and financial transactions, creating a privacy dilemma.
- Privacy concerns in the ML/AI world highlight the importance of individuals having control over their data usage and collection.
- Regulations like GDPR, CCPA, and HIPAA aim to protect data privacy but enforcing these rights in ML systems poses challenges, especially in terms of data deletion.
- The tension between model performance and ethical responsibility arises due to the need for massive datasets conflicting with privacy concerns.
- Over-collection of data by organizations for ML training raises issues of user privacy, data security, and regulatory compliance.
- Data silos resulting from reluctance to share data hinder the development of fair and unbiased AI systems, particularly affecting industries like healthcare and finance.
- Complex ML models lacking transparency pose privacy risks as decisions made by these models remain opaque and inexplicable.
- Privacy attacks targeting ML models aim to extract sensitive information from training data, posing significant threats to data confidentiality.
- Techniques like Federated Learning and Differential Privacy exist to mitigate privacy risks in ML models, but they can impact model performance.
- Achieving a balance between privacy and utility remains a major challenge in machine learning, as privacy-preserving techniques may affect model accuracy and generalizability.
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