Conclusion

Collection of data continues to expand rapidly, growing datasets into longer-term repositories with increasing value. However, this higher-dimension, longitudinal data creates a greater risk of privacy harms and the corresponding need to develop more privacy-protective techniques and technologies. The tension in providing detailed enough data to be useful while maintaining confidentiality of the underlying information will always remain. When datasets include potentially identifiable personal information, steps to prevent disclosure of this information can limit the extent to which researchers can analyze data with granular and accurate enough calculations.

Both private and public organizations have long relied on notice and consent and de-identification for protecting privacy—methods that have been shown to be no longer reliable. There are no silver bullets in disclosure limitation, and no single privacy-enhancing technique or technology will completely remove privacy risks. However, recent advances in disclosure limitation hold great promise for protecting confidentiality while allowing data to be used to provide valuable information. The emerging techniques of differential privacy and synthetic data can help move us forward from debates about anonymization and re-identification, towards a better balancing of data disclosure and confidentiality based on formalized and measurable metrics. Traditional disclosure limitation techniques are still of value as well and can be used in conjunction with modern methods to greatly reduce privacy risks.¹ However, the focus on personally identifiable information in current privacy regulations presents complications when considering disclosures protected by modern means such as differential privacy. Existing and future laws and policies will need to take account of the more quantifiable, comprehensive concepts of privacy that formal privacy methods provide. Researchers and policymakers will need to ask tough questions about how much statistical noise is enough to adequately protect privacy while still providing useful data, and how to capture and define these considerations in regulations and policies.