Published By: MIT News, 8/9/2016

Summary: Researchers at MIT and Indiana Univerity are developing differential privacy techniques that add a small amount of random noise to queries on large genetic databases. This means databases can be made more openly available without (as much) risking the privacy of the individuals whose genetic data is in them. Includes a description of the potential privacy attacks being mitigated, and notes the scientific consequences of having to worry about privacy-compromising attacks on health databases.

View the Article

CSP Global Impact Learning Objectives/EKs:
EK 7.2.1E Open and curated scientific databases have benefited scientific researchers.
EK 7.3.1G Privacy and security concerns arise in the development and use of computational systems and artifacts.
EK 7.3.1L Commercial and governmental curation of information may be exploited if privacy and other protections are ignored.

Other CSP Big Ideas:
2 Abstraction
3 Data and Information
4 Algorithms

Banner Image: “Network Visualization – Violet – Crop 5”, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0