Committee on Privacy and Confidentiality Committee on Privacy and Confidentiality Committee on Privacy and Confidentiality

ASA Committee on Privacy and Confidentiality

Key Terms/Definitions in Privacy and Confidentiality

Training Modules on Privacy and Confidentiality

Methods for Reducing Disclosure Risks

Protecting Biological and Health Data: Special Issues and Applications

Protecting Business and Tax Data: Special Issues and Applications

Protecting Demographic/Other Data: Special Issues and Applications

Guidelines for Government Statistical Agencies

Laws and Regulations about Privacy and Confidentiality

Human Subjects Protection, Ethical Research, and IRBs

 


Methods for Reducing Disclosure Risks When Sharing Data

Statistical agencies, survey organizations, academic researchers, and business establishments often collect data that they seek to share with others.  Dissemination of data can facilitate advances in research, inform public policy, furthers citizens' knowledge, and improve students' learning.   Typically, groups that share data are ethically or legally obligated to protect the confidentiality of data subjects' identities and sensitive attributes.  Failure to do so can break promises or violate laws, can cause data subjects to give lower-quality answers, and can reduce participation rates in future data collection efforts.  Data disseminators thus are pulled in two directions: the benefits of data access encourage them to release data, but the need to protect confidentiality encourages them not to release data.  The links below describe this dilemma and approaches to dealing with it.


A.  Defining the problem: Disclosure risk and data utility

Data providers strive to balance the quality of data for statistical analysis with the risk of confidentiality breaches. This page describes this trade off.

B.  Overviews of statistical disclosure protection methods
To protect confidentiality, data providers can alter the original data before sharing them. This page describes data alteration strategies typically used in practice.

C.  Technological solutions: secure data enclaves, remote access, data licensing
Another approach is to protect confidentiality by restricting or controlling who has access to the sensitive data. This page describes various mechanisms for providing restricted access.

D.  Computer science approaches and privacy preserving data mining
Computer scientists develop algorithms and protocols for safe sharing of data distributed across multiple parties, for safe query systems, and for general disclosure limitation setting. This page describes these approaches.

E.  Further investigation (books, journals, web sites, technical reports)
This page suggests some resources for further investigations of the topics in parts A through D.

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