
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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Initial Amendment Date: | September 19, 2012 |
Latest Amendment Date: | September 3, 2015 |
Award Number: | 1218085 |
Award Instrument: | Standard Grant |
Program Manager: |
Marilyn McClure
mmcclure@nsf.gov (703)292-5197 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2012 |
End Date: | September 30, 2016 (Estimated) |
Total Intended Award Amount: | $175,000.00 |
Total Awarded Amount to Date: | $175,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1000 OLD MAIN HL LOGAN UT US 84322-1000 (435)797-1226 |
Sponsor Congressional District: |
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Primary Place of Performance: |
4205 Old Main Hill Logan UT US 84322-1435 |
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): | CSR-Computer Systems Research |
Primary Program Source: |
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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
The emergence of cloud computing brings a paradigm shift to the way that data is stored, accessed and utilized. Especially, outsourcing data to the public cloud enjoys unlimited resources with great economic savings for both data owners and users. However, user privacy concerns have been a major hurdle for the widespread adoption of the public cloud technology. Encryption techniques can protect the confidentiality of users' data, however, supporting effective data utilization such as search operations over encrypted data become a key challenge. Existing techniques are either too computationally expensive, or lack enough flexibility to be adopted by cloud users in practice.
This project aims at protecting user privacy in the cloud. It develops the tools to provide privacy-assured, usable, and efficient data utilization services in outsourced cloud storage systems. Specifically, it tackles the above challenges by combining cryptography with information-retrieval techniques, and focuses on three aspects: (1) the design of novel keyword search schemes over encrypted data with rich functionalities, including ranked search and multi-keyword search; (2) the design of privacy-preserving search schemes over data that are represented using various structures, such as graphs; (3) new approaches for protecting user privacy in the mobile cloud setting. This research also includes a prototyping and experimentation plan.
Ensuring user privacy is fundamental to the success of public cloud deployment. This project also develops curricula and teaches and supervises students. Materials of this project will be made available online as tutorials, software packages, and publications of general interest.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project studies user privacy protection in the era of cloud computing. The objective is to develop the techniques and tools to provide privacy-assured, usable, and efficient data utilization services in cloud-based platforms and systems. Major research outcomes from this project include: 1) secure and efficient privacy-preserving search algorithms that support multiple keyword ranked search and multi-dimensional range queries over encrypted cloud data; 2) a series of new algorithms for geometric range search over encrypted spatial data, which support circular or arbitrary geometric range queries, and demonstrate higher efficiency and functionality comparing to the state-of-the-art solutions; 3) a new protocol for privacy-preserving distributed profile matching among multiple users in proximity-based mobile social networks; 4) a new privacy-preserving location proximity test protocol among users in a mobile cloud setting that achieves unforgeability of locations; 5) a highly efficient privacy-preserving set similarity evaluation protocol over encrypted datasets, which achieves verifiability of computation against an untrusted cloud server; 6) a new algorithm for privacy-preserving proximity test and two-hop user reachability evaluation over encrypted location data in the data outsourcing setting; 7) a new domain name system, OnioNS, for Tor– a widely used anonymous communication network, which is user-friendly, privacy-enhanced, decentralized, and secure.
The research results from this project have been widely disseminated to the research community and impacted the current active research on searchable encryption, secure computation outsourcing in cloud computing, secure multiparty computation in a distributed setting, and privacy-enhancing technologies in general. The research results have been published in prestigious journals and presentations have been made in top conferences. The paper, "Privacy-preserving multi-keyword text search in the cloud supporting similarity-based ranking", received the distinguished paper award at ACM ASIACCS conference in 2013, and the paper, “Privacy-preserving inference of social relationships from location data: a vision paper”, received the CCC Blue Sky Ideas award (third place) at the ACM SIGSPATIAL 2015 conference. In addition, the software code of OnioNS has been released online (licensed under the Modified BSD License). The PI has delivered multiple talks at various venues both in US and at overseas universities on the topics of the data security and privacy.
Last Modified: 11/01/2016
Modified by: Haitao Wang
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