Federated Clouds: A New Metric for Measuring the Quality of Data Anonymization
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Federated cloud has emerged as solution for cloud service providers to get
scalability in serving the growing demand for cloud resources. In a federated
cloud, a cloud member can provide service or request it from other cloud
provider members in the federation. The federation enables its cloud provider
members to be able to satisfy a service beyond the resources they owned by
using the resources market in the federation. Data privacy is a major concern
in federated clouds. As the privacy regulations and laws of the countries in the
federation may vary, it is difficult to assess and confirm that they are in
compliance. This makes protecting privacy even more challenging. Privacy
management strategies primarily involve anonymization, cryptography, and
data splitting. Anonymization is the traditional approach to preserving privacy,
which aims at masking the link between the quasi-identifier and sensitive data.
The most widely used anonymization techniques are k-anonymity, l-diversity
and t-closeness. However, there is a lack of a formal metric to measure the
quality of the anonymization process in terms of its ability to prevent reidentification.
This paper examines the issue of assessing anonymization
quality and introduces a new metric, Mmaq, for this purpose. It can be used to
evaluate the anonymization of one or multiple attributes. The metric is a
combination of the Shannon index, which measures diversity, and a stabilizer
factor, which corrects the Shannon index for pathological cases. The initial
results suggest that Mmaq can be used to classify attributes as identifier,
quasi-identifier, and anonymous. Furthermore, it can be employed as a Cloud
Privacy Policy anonymization compliance checker.
Keywords
QA75 Electronic computers. Computer science