Gender differences in attitudes among those at risk for Huntington's disease

This report presents and discusses selected findings regarding gender differences from an Australian-based study that investigated attitudes of individuals at risk for Huntington's disease (HD) towards genetic risk and predictive testing. Clear gender differences emerged regarding perceived coping capacity with regard to predictive testing, as well as disclosure of the genetic risk for HD to others. Female participants were more likely to disclose their genetic risk to others, including their medical practitioners, while male participants were three times more fearful of disclosing their genetic risk to others. These findings are of interest in light of gender differences that have consistently been reported regarding the uptake of predictive testing for HD, other genetic conditions, and health services more generally. While gender differences cannot provide a fully explanatory framework for differential uptake of predictive genetic testing, men and women may experience and respond differently to the genetic risk for HD and possibly other inherited disorders. The meanings of genetic risk to men and women warrants further exploration, given anticipated increases in genetic testing for more common conditions, especially if post-test interventions are possible. These issues are also relevant within the context of individuals' concerns about the potential for discrimination on the basis of genetic risk or genetic test information.

A border-based approach for hiding sensitive frequent itemsets

Sharing data among organizations often leads to mutual benefit. Recent technology in data mining has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that nonsensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach. © 2005 IEEE