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Details of Grant 

EPSRC Reference: GR/S22295/01
Title: HIPERSTAD: Using HIgh PERformance computing in STAtistical Disclosure control
Principal Investigator: Keane, Professor J
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Computer Science
Organisation: University of Manchester, The
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 2003 Ends: 31 March 2007 Value (£): 66,349
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Related Grants:
GR/S22790/01
Panel History:  
Summary on Grant Application Form
Many organisations require confidential personal information. However, simple measures, such as removing names and addresses, do not prevent the identification of individuals as the contents, or attributes, of some records are unique and have the potential to be matched directly with personal details from another database - consider, for example, a sixteen-year-old widow. The more such attribute sets (uniques) that a record contains and the smaller their size the greater the perceived risk of disclosure. All uniques (up to a specified size) must be identified in order to disguise or remove the most 'risky' records. An ESRC grant has funded the development of a sequential algorithm which locates all minimal uniques (uniques with no unique subsets) up to a user-specified size at record level. Minimal uniques are considered in order to avoid the use of redundant information. However, due to the demanding levels of execution time and data storage required to find all minimal uniques (a problem that is believed to be NP-Complete) this algorithm is restricted to small datasets. We propose to widen the application of the above algorithm by investigating the use of graph theory and parallel computing for developing techniques for shrinking the search space and reducing execution time.
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Organisation Website: http://www.man.ac.uk