VIA SCIENCE INC — Department of Defense SBIR Phase I: AF193-CSO1
VIA SCIENCE INC — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense 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 $49,996. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code AF193-CSO1 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $49,996
- Agency
- Department of Defense · Air Force
- Program / Phase
- SBIR · Phase I
- Topic
- AF193-CSO1
- Solicitation
- DoD SBIR X19.2
- NAICS
- —
- Place of performance
- MA
- Period
- 2019-12-12 → 2020-12-12
Description
VIA offers customers TAC™, a software platform to enable sophisticated, secure, privacy-protected data analysis. Unlike every other data access solution, TAC™ brings an analyst's algorithms to where data is located, runs analysis there, and returns only authorized answers back to the analyst. By providing analysts only remote access to data, there are no new copies subject to misuse. TAC™’s cornerstone is a set of nine pending patents for VIA-developed techniques in the field of privacy-protected federated analysis (i.e., simultaneous analysis of multiple distinct datasets). TAC™ offers a range of privacy protection features ranging from basic data disguising and anonymizing to mathematically verifiable techniques like Differential Privacy and 2048-bit RSA Additive Homomorphic Encryption. Through Blockchain-based Smart Contracting, data owners set and maintain comprehensive control over privacy protection settings and can audit compliance during and after completion of analysis processes. TAC™ offers sophisticated tools to speed cleaning data and harmonizing formats across multiple datasets, enabling analysts to work as if data had actually been centralized in one location. These tools include advanced AI models (e.g., T-SNE algorithms) to identify data issues, and secure Foreign Data Wrappers to allow data to be accessed across multiple database types.