| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 5, 2006 |
| Latest Amendment Date: | July 23, 2009 |
| Award Number: | 0627680 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Vijayalakshmi Atluri
CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | September 15, 2006 |
| End Date: | August 31, 2011 (Estimated) |
| Total Intended Award Amount: | $488,950.00 |
| Total Awarded Amount to Date: | $736,132.00 |
| Funds Obligated to Date: |
FY 2007 = $111,125.00 FY 2008 = $358,307.00 FY 2009 = $133,349.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
341 PINE TREE RD ITHACA NY US 14850-2820 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CYBER TRUST, ITR-CYBERTRUST, TRUSTWORTHY COMPUTING |
| Primary Program Source: |
app-0107 01000809DB NSF RESEARCH & RELATED ACTIVIT 01000809RB NSF RESEARCH & RELATED ACTIVIT 01000910DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Johannes Gehrke
Cornell University
0627680
Panel: P060970
CT: T Collaborative Research: Preserving Utility While Ensuring Privacy for Linked Data
Abstract
This research investigates how to publish data while limiting disclosure about entities in the data. An example is census data, an invaluable source of socioeconomic data. Simple approaches for limiting disclosure, such as removing identifying attributes like social security number and name, are not sufficient because combinations of other information in the data can help identify individuals in the data, especially when the data can be linked to external databases. It is this linkage, and in general, the property of data that it is often explicitly linked to other data, that is the focus of this project.
In linked data, data records are linked through relationships between records. Examples include data about students and the classes they took where the links are the association between a student and the classes she took; data about network packets and the routers that forwarded these packets, where the links are the association of packets to routers; or data about people and their social network, where the links are the social relationships between people. It is the explicit representation of these links in the data that violates some of the key assumptions of prior work. This research spans the whole spectrum from motivating applications of linked data, to novel privacy models and practical anonymization algorithms, to new techniques for attacking and analyzing anonymized data.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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