Name
Abstract | ||
|---|---|---|
Novel sensor processing algorithms face many hurdles to their adoption. Sensor processing environments have become increasingly difficult with an ever increasing array of threats. These threats have, in turn, raised the bar on deploying new capabilities. Many novel sensor processing algorithms exploit or induce randomness to boost algorithm performance. Co-designing this randomness with cryptographic features could be a powerful combination providing both improved algorithm performance and increased resiliency. The emerging field of signal processing in the encrypted domain has begun to explore such approaches. The development of this new class of algorithms will require new classes of tools. In particular, the foundational linear algebraic mathematics will need to be enhanced with cryptographic concepts to allow researchers to explore this new domain. This work highlights a relatively low overhead method that uses homomorphic encryption to enhance the resiliency of a part of a larger sensor processing pipeline. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/HPEC43674.2020.9286175 | 2020 IEEE High Performance Extreme Computing Conference (HPEC) |
Keywords | DocType | ISSN |
sensor data,homomorphicencryption,multiparty secure computing,matrix operations | Conference | 2377-6943 |
ISBN | Citations | PageRank |
978-1-7281-9220-8 | 0 | 0.34 |
References | Authors | |
15 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vijay Gadepally | 1 | 449 | 50.53 |
| Mihailo Isakov | 2 | 0 | 0.34 |
| Rashmi S. Agrawal | 3 | 0 | 0.34 |
| Jeremy Kepner | 4 | 606 | 61.58 |
| Karen Gettings | 5 | 0 | 0.34 |
| Michel A. Kinsy | 6 | 189 | 31.55 |