| EPSRC Reference: |
EP/J020389/1 |
| Title: |
Game Theoretic Privacy-Preserving Collaborative Data Mining |
| Principal Investigator: |
Phan, Professor RC |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Electronic, Electrical & Systems Enginee |
| Organisation: |
Loughborough University |
| Scheme: |
Standard Research |
| Starts: |
21 May 2012 |
Ends: |
20 May 2013 |
Value (£): |
120,189
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Digital Signal Processing |
| Information & Knowledge Mgmt |
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| EPSRC Industrial Sector Classifications: |
| Aerospace, Defence and Marine |
Information Technologies |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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09 Feb 2012
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Data Intensive Systems (DaISy)
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Announced
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Summary on Grant Application Form |
This proposal focuses on the problem setting where coalition parties, each owning a large set of data, desire to discover new knowledge when they collaborate to jointly process all the datasets; while ensuring that each individual dataset is not revealed to the other parties.
Solutions to this problem are key enablers for ensuring smooth cooperation among parties who do not necessarily trust each other fully. Example situations that reflect this need include coalition forces on military or peace-keeping missions, nations joining forces to detect and prevent terrorism activities, while not willing to reveal their actual intelligence data on national security, and organisations collaboratively analysing consumer behaviour while keeping their customers' profiles private.
The proposed research addresses challenges within the three themes of the DaISy Call, notably those aimed at secure and privacy-preserving collaborative extraction of meaning from data intensive systems comprising different parts of data owned by adaptively changing coalition partners.
We aim to develop novel techniques with guarantees of privacy that will perform data mining even when data are in encrypted form, including the specific tasks of clustering, dependency modelling, classification, regression, and association. The behaviour of such coalition parties involved in performing joint data mining will be analysed using a game theoretic framework, and various innovative collaborative data mining techniques will be developed using this framework while ensuring that privacy is preserved, even when some coalition members may collude. Our techniques will also be designed to be adaptive to and efficient when coalition membership changes.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.lboro.ac.uk |