CRII: SaTC: Towards Cost-Efficient Private Computation for Cloud Data Science — NSF Award to CUNY City College (NY, $164,999)
Collaborative data science leads to scientific advancement: the better the data, the better the science. Cryptographic protocols such as secure multiparty computation (MPC) enable collaborative data science on private datasets for richer scientific discovery, especially in fields with downstream societal relevance such
| Award title | CRII: SaTC: Towards Cost-Efficient Private Computation for Cloud Data Science |
|---|---|
| Award ID | 2451597 |
| Awardee | CUNY City College |
| City | NEW YORK |
| State | NY |
| Amount obligated | $164,999 |
| Principal investigator | Tushar Jois |
| Program | Secure &Trustworthy Cyberspace |
| Start date | 07/01/2025 |
| Abstract | Collaborative data science leads to scientific advancement: the better the data, the better the science. Cryptographic protocols such as secure multiparty computation (MPC) enable collaborative data science on private datasets for richer scientific discovery, especially in fields with downstream societal relevance such as genomics. MPC, however, incurs non-trivial computational overhead; so, in the face of increasing cloud computing costs, data scientists are disincentivized from adopting MPC. T |
| Source | NSF Awards |
$799/mo
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