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Studying Genotype-Phenotype Attack on k-anonymised Medical and Genomic Data

Baig, M. M., Li, J., Liu, J. and Wang, H.

    Personal data of patients is largely collected at hospitals, clinics, labs, etc. This data consists of medical and genomic record. Such patient data is shared for various health and research purposes. The utility of such sharing is worthwhile and its benefits are now well documented. It includes early diagnostic of some diseases like Phenylketonuria that can cause high chances of recovery. Population health analysis, derived from collaborative sharing of patient data help government agencies to draft proper policies to raise the standard of living of people. On the other side of the picture, many patients fear about the misuse of their personal data. The fear caused social (sometimes legal) requirement to properly safeguard the personal data before sharing. Various generalization techniques were suggested to anonymize the both types of patient data i.e. medical and genomic. Generalization based privacy protection technique, k-anonymity is considered to be one of important practices to anonymize patient data. Due to rapid technological advancements, it is possible that the medical and genomic data of same patient(s) can be publically available from different sources. Such a scenario has created new privacy threats to patient data. Genotype-Phenotype attack is one of these threats. This research paper shows how k-anonymized medical and genomic data is subject to genotype-phenotype attack.
Cite as: Baig, M. M., Li, J., Liu, J. and Wang, H. (2009). Studying Genotype-Phenotype Attack on k-anonymised Medical and Genomic Data. In Proc. Eighth Australasian Data Mining Conference (AusDM`09) Melbourne, Australia. CRPIT, 101. Kennedy P. J., Ong K. and Christen P. Eds., ACS. 159-166
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