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The Ultimate Guide to Privacy Preserving Technologies
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Enterprises must leverage and share their sensitive data assets for maximum economic return, while ensuring privacy, confidentiality and compliance. While traditional privacy techniques such as data masking or using an expert determination may feel familiar and “good enough”, they often severely restrict access and de-value your data. The last several years have seen an emergence of new techniques that achieve similar goals with higher value from data and lower risk for the enterprise.
This guide will help you understand these newer approaches and how they map to data use scenarios. The guide also contains links to additional resources that dive into further details in this exciting space. Coverage includes:
- Differences you may consider when sharing data internally vs externally
- Various data sharing access modes and how they impact considerations for privacy preserving analytics
- A review of technologies that can be used to ensure privacy while sharing data including:
- Differential privacy
- De-identification
- Data Anonymization and Data Masking
- Homomorphic encryption
- Federated learning
- Secure Multi-party computation
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