Privacy-preserving machine learning enables learning and inference while limiting exposure of personal data. It supports customer insights and personalization without directly accessing or centralizing raw individual records. section 2.4 section 2.2.2
Common approaches shift where data is stored, how it is computed, or what outputs reveal:
- Federated learning and on-device computation keep raw data locally and share only aggregated model updates. section 2.4 section 2.2.2
- Differential privacy adds statistical noise to aggregated data to reduce the risk of identifying individuals. section 2.2.2
- Homomorphic encryption permits computation on encrypted data without exposing the underlying sensitive information. section 2.2.2
- Secure multiparty computation allows parties to derive results without directly sharing their private inputs. section 2.4
Effective use requires matching the technique to the use case and reinforcing it with consent, data governance, user controls, and secure processing. Privacy protection does not remove every risk: model unlearning can be difficult, while biased training data and opaque outputs can create ethical and compliance problems. section 2.4 section 2.2.2
Customer churn prediction
A company can train a churn model across customer devices or organizational data stores without centralizing sensitive records, sharing aggregated updates instead. The same pattern can support product recommendations and audience insights. section 2.4 section 2.4
