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Privacy Protection for a Brain Imaging Databank

Jyothsna Vivekanand Shenoy

In recent years there has been an increasing trend towards releasing micro-data to the public. This can be very important for research, but in some cases (e.g. medical data) these releases are limited due to privacy protection issues. Anonymisation is a limited solution that does not fully protect the individuals. Even when all the personal identifiers have been removed it might be possible to identify an individual from an anonymous records using quasi-identifiers and data linking with some other external data source (see references).

In order to build a Normative Brain Imaging Bank we plan to collect a considerable amount of brain imaging, clinical, demographic and other data. To fully exploit the potential of such a resource the data has to be shared with other researchers.

The project consists in exploring the privacy risks of such a resource. The student will have access to the database schema, but not to the actual data. She will have to produce realistic simulated data to test her solutions. This solution will be a recomendation on what data items is safe to release etc.

Project status: 
Degree level: 
Knowledge of databases. Programming skills.
Supervisors @ NeSC: 
Student project type: 
Fung, Benjamin C. M. and Wang, Ke and Chen, Rui and Yu, Philip S. "Privacy-preserving data publishing: A survey of recent developments" ACM Computing Surveys, Vol. 42, No. 4, Article 14 B.-C. Chen, D. Kifer, K. LeFevre and A. Machanavajjhala. "Privacy-Preserving Data Publishing" Foundations and TrendsR in Databases Vol. 2, Nos. 1–2 (2009) 1–167 L. Sweeney. "k-Anonymity: a model for protecting privacy". In International Journal on Uncertainty, Fuzziness and Knowledgebased Systems, 10(5), pages 557-570, 2002 Samarati P (2001). "Protecting respondents' identities in microdata release". IEEE Transactions on Knowledge and Data Engineering, 13(6):1010{1027