Award Abstract # 1218085
CSR: Small: Collaborative Research: Towards User Privacy in Outsourced Cloud Data Services

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UTAH STATE UNIVERSITY
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: FY 2012 = $175,000.00
History of Investigator:
  • Haitao Wang (Principal Investigator)
    haitao.wang@utah.edu
  • Ming Li (Former Principal Investigator)
Recipient Sponsored Research Office: Utah State University
1000 OLD MAIN HL
LOGAN
UT  US  84322-1000
(435)797-1226
Sponsor Congressional District: 01
Primary Place of Performance: Utah State University
4205 Old Main Hill
Logan
UT  US  84322-1435
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): SPE2YDWHDYU4
Parent UEI:
NSF Program(s): CSR-Computer Systems Research
Primary Program Source: 01001213DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7923, 9150
Program Element Code(s): 735400
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

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Boyang Wang, Ming Li, and Haitao Wang "Geometric Range Search on Encrypted Spatial Data" IEEE Transactions on Information Forensics and Security , v.11 , 2016 10.1109/TIFS.2015.2506145
Ming Li, Shucheng Yu, Kui Ren, Wenjing Lou and Y. Thomas Hou "Toward Privacy-Assured Searchable Cloud Data Storage Services" IEEE Network , v.27 , 2013 , p.N/A
Ming Li, Shucheng Yu, Ning Cao and Wenjing Lou "Privacy-Preserving Distributed Profile Matching in Proximity-based Mobile Social Networks" IEEE Transactions on Wireless Communications (TWC) , v.12 , 2013 , p.2024-2033 10.1109/TWC.2013.032513.120149
Ning Cao, Cong Wang, Ming Li, Kui Ren and Wenjing Lou "Privacy-Preserving Multi-keyword Ranked Search over Encrypted Cloud Data" IEEE Transactions on Parallel and Distributed Systems (TPDS) , v.25 , 2014 , p.222
Wenhai Sun, Bin Wang, Ning Cao, Ming Li, Wenjing Lou, Y. Thomas Hou and Hui Li "Verifiable Privacy-Preserving Multi-keyword Text Search in the Cloud Supporting Similarity-based Ranking" IEEE Transactions on Parallel and Distributed Systems (TPDS) , v.99 , 2013

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

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page