Balancing data privacy and usability in the federal statistical system

Proceedings of the National Academy of Sciences · 2022 · 53 citations

• Computer Science
• Computer Security
• Internet privacy

The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit-cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.

What’s the Inside Scoop? Challenges in the Supply and Demand for Information on Employers

Journal of Labor Economics · 2022 · 28 citations

• Computer Science
• Labour economics
• Economics

Workers struggle to understand prospective employers. Through experienced workers’ volunteered reviews, Glassdoor is a platform seeking to provide information about prospective employers to job seekers. We find that the content most valuable to job seekers (negative information) is the kind most risky to supply, pointing to a catch-22. Higher ratings increase job applications to smaller firms only, creating an incentive for them to discourage negative reviews. Concerns about employer retaliation discourage negative reviews and motivate employees who do disclose to conceal aspects of their identity, degrading the information’s value. Reputation institutions provide valuable but partial solutions to workers’ information problems.