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(4) Deep Defense of Distributed Deep Learning (D4L)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0169
Agency Tracking Number: N193-A01-0324
Amount: $139,974.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-A01
Solicitation Number: 19.3
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-21
Award End Date (Contract End Date): 2020-04-20
Small Business Information
270 Point Rd
Eclectic, AL 36024
United States
DUNS: 809220622
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Charles O'Neill
 Director of Research Engineering
 (617) 449-8206
Business Contact
 Edward Hackett
Phone: (703) 943-7205
Research Institution

This project aims to achieve trustworthy deep learning (TDL)-based distributed computation among wireless connected Naval devices (sensors, UAVs, mobile phones, ships, etc.) through the seamless integration of cryptography, a complimentary classifier, and communications (C4). Distributed Deep Learning (DDL) runs in a distributed Naval network setting and is vulnerable to attacks that have knowledge of DDL meta-parameters. A simple generative adversarial network (GAN)-based model can cause the DDL to leak label information to adversaries. Key-based gradient encryption method does not include a complete DDL network security protocol. Blockchain may be used to protect DL; however, this approach does not have learning-based input control and cannot resist adaptive spiral attacks in a complex DDL setting. These elements enable a new challenging attack that is unique to the DDL environment: the adaptive grey-box spiral (AGBS) attack. Our project goal is to develop and demonstrate C4, a powerful new method that seamlessly integrates applied Cryptography, distributed deep Computation, a Complementary Classifier with ensemble Bayesian learning, and a Naval Communication scheme with Blockchain protocol.

* Information listed above is at the time of submission. *

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