Marketing teams may need insights from sensitive customer data without giving analysts access to the raw records. Homomorphic encryption allows specified computations on encrypted data without decrypting it first; the decrypted result corresponds to a computation on the original data. section 2.3.3 section 2.4
It is one option among privacy-enhancing technologies, each addressing a different exposure risk:
- Homomorphic encryption keeps data encrypted during supported computations. section 2.3.3
- Federated learning keeps raw training data on devices rather than centralizing it; differential privacy adds noise to protect individuals in aggregated analysis. section 2.2.2
- Data minimization limits what is collected or used in the first place. Encryption does not replace that restraint or sound data governance. section 2.2.2 section 2.3.3
Start with a bounded operation
Validation or indexing illustrates the narrower operations currently possible on encrypted data. Microsoft SEAL is named as an example solution, but full-scale analysis of encrypted data is not yet mainstream. Select the technique for the operation needed, rather than assuming it can support every marketing analysis. section 2.3.3 section 2.2.2
