Generative AI, such as chatbots and image creators, have great potential but also raise privacy concerns. To address these issues, several strategies can be implemented. Data anonymization involves removing personal identifiers from data before feeding it into AI models. De-identification takes this a step further to ensure that even with additional information, individuals cannot be identified. Federated learning allows AI to learn from data on multiple devices without centralizing the data, thereby protecting privacy. Differential privacy adds noise to data to prevent individual details from standing out. Data encryption encodes data so only authorized parties can access it, whether in transit or at rest. Access controls and auditing, such as Role-Based Access Control and audit logs, ensure that only authorized individuals can access sensitive data and track all actions for accountability. By implementing these strategies, we can harness the power of generative AI while safeguarding data privacy.
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Source link: https://medium.com/@sainitesh/how-to-ensure-data-privacy-in-generative-ai-32a770213cda?source=rss——artificial_intelligence-5
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Securing data privacy in Generative AI: Tips and tricks #DataPrivacyAI
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