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SBIR Phase I: A System for Privacy-Preserving Data Mining from Multi-Party…

Award Information

Agency:
National Science Foundation
Branch:
N/A
Award ID:
74483
Program Year/Program:
2005 / SBIR
Agency Tracking Number:
0441729
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Agnik, LLC
8840 Stanford Blvd. Suite 1300 Columbia, MD 21045
View profile »
Woman-Owned: No
Minority-Owned: Yes
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2005
Title: SBIR Phase I: A System for Privacy-Preserving Data Mining from Multi-Party Distributed Data
Agency: NSF
Contract: 0441729
Award Amount: $97,404.00
 

Abstract:

This Small Business Innovation Research (SBIR) Phase I research project will develop a collection of privacy sensitive distributed data mining algorithms for immediate applications in domains that deal with sensitive private data. Privacy is becoming a growing concern in many data monitoring and mining applications such as network intrusion detection, fraud detection, and counter-terrorism intelligence gathering among others. However, to date, there does not exist any commercial data mining system that is capable of analyzing potentially distributed multi-party data in a privacy-sensitive manner. This research will develop technology to meet this immediate need. It will develop data mining algorithms that can work without direct access to the original sensitive data. The research will particularly focus on privacy-preserving statistical computing and clustering techniques that are particularly suitable for security-related threat management applications. The algorithmic approach is based on a combination of random projection and secured multi-party computation-based techniques. Deliverables will include a collection of privacy-sensitive algorithms and a documentation of their performance along with a demonstration. A successful completion of this project will open up many new possibilities particularly in the domain of security and threat management for counter-terrorism which are not possible today because of due concerns about the privacy of the common citizens. Privacy-preserving data mining has numerous potential applications, with enormous potential benefit for security and economic efficiency. It also has the great virtue of offering transparency to providers of information, allowing them to understand and control the revelation of sensitive features.

Principal Investigator:

Kakali Sarkar
Ms
4102900146
kakali@agnik.com

Business Contact:

Kakali Sarkar
Ms
4102900146
kakali@agnik.com
Small Business Information at Submission:

AGNIK
8840 Stanford Blvd Suite 1300 Columbia, MD 21045

EIN/Tax ID:
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No