Name
Abstract | ||
|---|---|---|
Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensitive and can be used to track individuals. In this work, we design Poirot, a system to collect aggregate statistics about physical interactions in a privacy-preserving manner. We show a preliminary evaluation of our system that demonstrates the scalability of our approach even while maintaining strong privacy guarantees.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3384419.3430603 | SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems
Virtual Event
Japan
November, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7590-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanping Zhang | 1 | 0 | 0.34 |
| Chenghong Wang | 2 | 54 | 9.71 |
| David Pujol | 3 | 0 | 1.69 |
| Johes Bater | 4 | 0 | 1.35 |
| Matthew Lentz | 5 | 42 | 4.05 |
| Ashwin Machanavajjhala | 6 | 2624 | 132.52 |
| Kartik Nayak | 7 | 185 | 9.20 |
| Lavanya Vasudevan | 8 | 0 | 0.34 |
| Jun Yang | 9 | 2762 | 241.66 |