STEALTH SOFTWARE TECHNOLOGIES, INC. — Department of Commerce SBIR Phase I: 3
STEALTH SOFTWARE TECHNOLOGIES, INC. — SBIR Phase I award from Department of Commerce.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Commerce in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $99,989. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code 3 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $99,989
- Agency
- Department of Commerce · National Institute of Standards and Technology
- Program / Phase
- SBIR · Phase I
- Topic
- 3
- Solicitation
- 2021-NIST-SBIR-01
- NAICS
- —
- Place of performance
- CA
- Period
- 2021-08-01 → 2022-03-31
Description
Stealth Software Technologies, Inc. (Stealth) will develop a prototype software platform for performing secure statistical computations on datasets held by multiple mutually untrusting parties, enabling analysis and extraction of information derived from previously “siloed” data, without compromising the privacy of the underlying data. This software will make use of cryptographic techniques in Secure Multiparty Computation to ensure that no party’s private information is leaked to other parties during the computation. The system’s computational back-end will incorporate recent cryptographic research that enables more efficient Private Set Intersection than existing protocols. In order to increase accessibility and usability, the user interface will allow multiple users without expertise in cryptography to configure the privacy and computational parameters of a joint analysis of their separately held data, and the system will integrate with commonly used existing relational databases. The resulting system will allow researchers and analysts the full capability of performing the desired analyses on the joined data without requiring direct access to other parties’ data, thereby maintaining the security and privacy requirements present where private datasets are spread across multiple siloed databases.