Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness.

Key features:

• Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation

• Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives

• Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy

• Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches

The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.

Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness.

Key features:

• Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation

• Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives

• Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy

• Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches

The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.

Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness.

Key features:

• Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation

• Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives

• Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy

• Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches

The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.

Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature—specifically, synthetic data, formal privacy, and secure computation—can be used to manage trade-offs in disclosure risk and data usefulness.

Key features:

• Provides overviews of the potential and the limitations of synthetic data, differential privacy, and secure computation

• Offers an accessible review of methods for implementing differential privacy, both from methodological and practical perspectives

• Presents perspectives from both computer science and statistical science for addressing data confidentiality and privacy

• Describes genuine applications of synthetic data, formal privacy, and secure computation to help practitioners implement these approaches

The handbook is accessible to both researchers and practitioners who work with confidential data. It requires familiarity with basic concepts from probability and data analysis.