A theory and toolkit for the mathematics of privacy : methods for anonymizing data while minimizing information loss

Privacy laws are an important facet of our society. But they can also serve as formidable barriers to medical research. The same laws that prevent casual disclosure of medical data have also made it difficult for researchers to access the information they need to conduct research into the causes of disease. But it is possible to overcome some of these legal barriers through technology. The US law known as HIPAA, for example, allows medical records to be released to researchers without patient consent if the records are provably anonymized prior to their disclosure. It is not enough for records to be seemingly anonymous. For example, one researcher estimates that 87.1% of the US population can be uniquely identified by the combination of their zip, gender, and date of birth - fields that most people would consider anonymous. One promising technique for provably anonymizing records is called k-anonymity. It modifies each record so that it matches k other individuals in a population - where k is an arbitrary parameter. This is achieved by, for example, changing specific information such as a date of birth, to a less specific counterpart such as a year of birth.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Katirai, Hooman

Advisor dc:contributor.advisor

• Peter Szolovits.

• Technology and Policy Program.
• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/34526

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/34526