Understanding privacy attacks
Unlike poisoning, tampering, and evasion attacks, privacy attacks do not seek to alter the model in any way. Instead of targeting model integrity, these attacks focus on model confidentiality and extracting sensitive information, which, in turn, can seriously impact the trust users and businesses place in these AI systems. As a result, they represent a significant challenge, posing risks to individual privacy, organizational security, and competitive advantage. These attacks come in the form of model extraction, model inversion, and membership inference, and each targets AI systems in unique ways:
- Model extraction attacks: These attacks involve replicating an AI model’s functionality by observing its responses to various inputs. The primary risk here is the potential loss of intellectual property and the unauthorized duplication of proprietary AI models, which can have significant financial and competitive implications.
- Model inversion...
