5 Data sharing

We have so far worked to implement privacy engineering by focusing on data. In the preceding chapters, we have classified data based on risk and then tagged it using machine-readable tags. This process, both in its planning and execution, represents a significant investment. The reason for this effort is that data is used by humans and their algorithmic processes at scale to make decisions that impact the users whose data it is.

The other key benefit of this data governance is that companies can share data with privacy protections tailored to the risk. In this chapter, we will first take a look at why companies may share data. We will look at a use case that speaks to a key part of online commerce—the online ads ecosystem.

Then, we will look at a real-life scenario where data-sharing resulted in privacy risks. We will then explore techniques that will help reduce privacy risk when sharing data, and we’ll explore the limitations of such privacy-preserving techniques. We will also explore how you can measure the privacy impact of sharing data and how such techniques can, in a numerically provable fashion, reduce privacy risk.

5.1 Data sharing: Why companies need to share data

5.1.1 Data sharing: Taxicab companies

5.1.2 Data sharing: Online advertising

5.1.3 Privacy in advertising

5.2 How to share data safely: Security as an ally of privacy

5.2.1 Tracking President Trump

5.2.2 Protecting data in motion

5.2.3 Protecting data at rest

5.3 Obfuscation techniques for privacy-safe data sharing

5.3.1 Data sharing and US national security

5.3.2 Data anonymization: The relationship between precision and retention

5.5.1 K-anonymity