gender differential paper IJCRB

ijcrb.webs.com
INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS
JUNE 2011
VOL 3, NO 2
DATA LOSS AND PROBABILITY OF THREATS ASSOCIATED
WITH K-ANONYMITY
Tariq Sadad
MSCS, Department of Computer Science, International Islamic University, Islamabad
Azhar Rauf
Assistant Professor, Department of Computer Science, University of Peshawar, Peshawar
Ayyaz Hussain
Assistant Professor, Department of Computer Science, International Islamic University,
Islamabad
Abstract
K-anonymity provides a model to protect the confidentiality of the individual. It presents the data
in such a way that for each single tuple there are k same types of tuples in the released table. In
this paper, we calculate the total data loss in a table after applying k-anonymity. A measure is
given that can calculate data loss for any value of K. Similarly the probability of threats is also
measured after applying K-anonymity. This work also measures the strength of security. These
measurements help a Database Administrator or Security Officer to select the value of K for
applying K-anonymity based on the security and data loss requirements of an organization. It is
proved that K-anonymity is directly proportional to data loss and security, and inversely
proportional to probability of threats.
Key words: K-anonymity, Quasi-identifier, Data Loss, Probability of Threats
1. INTRODUCTION
Currently different organizations such as hospitals publish their raw non-aggregated data (also
called micro data), for research purpose or a variety of different reasons. However, such data
may contain private information as in the case of medical record, where identities of entities
should be kept secret. In 1996, TIME/CNN conducted telephone poll in United States in which
88% of the respondents replied that medical information about themselves should not be released
without their permission. In a second survey, 87% people said to restrict organizations from
giving out medical information without a patient’s permission. People prefer that employees and
directly involved people can only have access to their records and organization should be
bounded by ethical and legal standards to prohibit further disclosure of their data (Sweeney,
1997)
Currently, the leakage of health information is rigorously regulated in many jurisdictions.
Organizations are required to apply privacy protection to health data earlier to their revelation to
researchers. For example, the Health Insurance Portability and Accountability Act (HIPAA) in
the United States, and the Personal Health Information Protection Act (PHIPA) in Canada, are
some of the well known privacy regulations that protect the confidentiality of healthcare
information.
In order to protect the data of respondents, data holders often remove or encrypt explicit
identifiers such as names, SSN and phone numbers before releasing (Ciriani, Foresti and
COPY RIGHT © 2011 Institute of Interdisciplinary Business Research 302