Big Data for Healthcare
Content
Big Data for Healthcare
Abstract
Big data techniques are spreading also in medical research. By these techniques is possible
extract information from complex heterogeneous sources, realizing longitudinal studies focused to
correlate the patient status with biometric parameters. The rise of big data, however, also raises
challenges in terms of privacy, security, data ownership, data stewardship, and governance. In this
paper, we propose a secure and private data management framework that addresses both the security
and privacy issues in the management of medical data in outsourced databases. The proposed
framework ensures the security of data by using semantically-secure encryption schemes to keep
data encrypted in outsourced databases.
There has been a tremendous growth in health data collection since the development of
Electronic Medical Record (EMR) systems. Such collected data is further shared and analysed for
diverse purposes. Despite many benefits, data collection and sharing have become a big concern as
it threatens individual privacy. The framework also provides a differentially-private query interface
that can support a number of SQL queries and complex data mining tasks. We experimentally
evaluate the performance of the proposed framework, and the results show that the proposed
framework is practical and has low overhead.
Abstract
Big data techniques are spreading also in medical research. By these techniques is possible
extract information from complex heterogeneous sources, realizing longitudinal studies focused to
correlate the patient status with biometric parameters. The rise of big data, however, also raises
challenges in terms of privacy, security, data ownership, data stewardship, and governance. In this
paper, we propose a secure and private data management framework that addresses both the security
and privacy issues in the management of medical data in outsourced databases. The proposed
framework ensures the security of data by using semantically-secure encryption schemes to keep
data encrypted in outsourced databases.
There has been a tremendous growth in health data collection since the development of
Electronic Medical Record (EMR) systems. Such collected data is further shared and analysed for
diverse purposes. Despite many benefits, data collection and sharing have become a big concern as
it threatens individual privacy. The framework also provides a differentially-private query interface
that can support a number of SQL queries and complex data mining tasks. We experimentally
evaluate the performance of the proposed framework, and the results show that the proposed
framework is practical and has low overhead.
