Identifying Participants in the Personal Genome Project by Name (A Re-identification Experiment)
By Latanya Sweeney, A. Abu, Julia Winn. 2013
219 citations recorded by the source corpus.
We linked names and contact information to publicly available profiles in the Personal Genome Project. These profiles contain medical and genomic information, including details about medications, procedures and diseases, and demographic information, such as date of birth, gender, and postal code. By linking demographics to public records such as voter lists, and mining for names hidden in attached documents, we correctly identified 84 to 97 percent of the profiles for which we provided names. Our ability to learn their names is based on their demographics, not their DNA, thereby revisiting an old vulnerability that could be easily thwarted with minimal loss of research value. So, we propose technical remedies for people to learn about their demographics to make better decisions.
Subfields: Cancer Research, Genetics, Public Health, Environmental and Occupational Health, Privacy & Security, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
In Public Health, Environmental and Occupational Health
In Privacy & Security
- Lalitha Sankar
- Chong Huang
- Peter Kairouz
- Chloé-Agathe Azencott
- Alexander Barg
- Min Ye
- Sewoong Oh
- Pramod Viswanath
In Computational Biology & Bioinformatics
Citations in the corpus
- Machine learning and genomics: precision medicine versus patient privacy (2018, background): “This made it possible to leverage voter registration data to find the names of 84 to 97% of the 579 participants to the Personal Genome Project who had listed these three pieces of demographic information [27].”
- Context-Aware Generative Adversarial Privacy (2017, methodology): “In a more recent attack, [78] showed that participants of an anonymized DNA study were identi ed by linking their DNA data with the publicly available Personal Genome Project dataset.”
- Why Big Data Won't Cure Us (2013, unknown): “a team that was able to link names and contact information to the publically available profiles in the Personal Genome Project through exploiting known weaknesses in large-scale demographic datasets.(15) By mining public records for the seemingly less innocuous information of birth date, gender, and”