| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | April 8, 2014 |
| Latest Amendment Date: | May 7, 2018 |
| Award Number: | 1350324 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Wei-Shinn Ku
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | June 1, 2014 |
| End Date: | May 31, 2021 (Estimated) |
| Total Intended Award Amount: | $471,164.00 |
| Total Awarded Amount to Date: | $471,164.00 |
| Funds Obligated to Date: |
FY 2015 = $92,518.00 FY 2016 = $94,199.00 FY 2017 = $95,912.00 FY 2018 = $97,662.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
ONE CASTLE POINT ON HUDSON HOBOKEN NJ US 07030-5906 (201)216-8762 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Castle Point on Hudson Hoboken NJ US 07030-5954 |
| 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): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
01001516DB NSF RESEARCH & RELATED ACTIVIT 01001617DB NSF RESEARCH & RELATED ACTIVIT 01001718DB NSF RESEARCH & RELATED ACTIVIT 01001819DB 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
Spurred by developments such as cloud computing, there has been considerable interest in the data-mining-as-a-service (DMaS) paradigm in which a client outsources his/her data mining needs to a third-party service provider. However, this raises a few security concerns. One of the security concerns is that the service provider may return plausible but incorrect mining results to the client. There is a crucial need for techniques that enable the client to verify, without much effort, that the service provider has performed the outsourced computations faithfully and returned correct mining results. Despite the recent intensive efforts on verifiable general-purpose computations, efficient result verification of data mining computations remains a largely unexplored territory.
This CAREER proposal aims at designing efficient and practical verification techniques for data mining computations outsourced to an untrusted service provider. Research activities include developing (1) innovative verification approaches for data mining computations without any privacy preservation mechanisms; (2) new verification approaches for privacy-preserving data mining computations; (3) novel methods for the analysis of attack types and modeling of the collusion behaviors of service providers; and (4) a full system approach in developing, deploying, and evaluating the proposed techniques.
Advances in verifiable outsourcing of data mining computations can spur wider adoption of cloud services. This project also includes curriculum development and the training of high school, undergraduate, graduate, women and students in underrepresented groups.
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.
Data Mining as a Service (DMaaS) allows clients with limited resources to outsource their expensive data mining tasks to powerful servers. Despite the huge benefits, current DMaaS solutions still lack strong assurances on service correctness (i.e., whether the DMaaS service providers faithfully execute the data mining tasks as expected). Without these assurances, unfaithful service providers can return improperly-executed data mining results or partially-trained data mining models while asking for over-claimed rewards. However, it is challenging to verify the integrity of service provider, especially in the open market without a trusted third party.
Intellectual Merit: The goal of this CAREER project is to design efficient and practical verification techniques to bring integrity assurances to DMaaS. With these techniques, the clients can be assured that data mining tasks are correctly executed on an untrusted server. The project has four main research tasks: (1) developing theory and algorithms for correctness verification of data mining computations without privacy constraint; (2) designing verification methods for privacy-preserving data mining computations in DMaaS; (3) analyzing and modeling the attacks and misbehaviors of DMaaS service providers; and (4) providing a full systematic approach in developing, deploying, and evaluating the proposed verification techniques. The work supported by this CAREER Award has led to quantitative new insights on the behaviors of state-of-the-art data mining models, greatly enhancing our capabilities to implement verification techniques for these models in DMaaS systems. The findings from this CAREER project have been published in 27 peer-reviewed journals and conference proceedings. This NSF CAREER award also leads to four PhD dissertations and 28 paper presentations in conferences.
Broader Impacts: With the support by this NSF CAREER award, the PI has been able to develop a highly interdisciplinary and collaborative research and education program that involves broad participation of graduate and undergraduate students. Furthermore, the PI has incorporated new course materials relevant to the PI's research into undergraduate (CS442: Database Management System) and graduate (CS609: Data Management and Exploration on the Web) courses at Stevens Institute of Technology (SIT). The PI also has provided training of female students through this CAREER project. With this CAREER award, the PI has been working with two female master students for their independent study at SIT, and graduated one female PhD student.
Last Modified: 10/06/2021
Modified by: Wendy Hui Wang
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