@article{nokey,

title = {Privacy-Preserving Collision Detection for Drone-based Aerial Package Delivery using Secure Multi-Party Computation},

author = {Anushka Desai and Oscar G Bautista and Kemal Akkaya},

url = {https://dl.acm.org/doi/abs/10.1145/3565287.3617631},

year  = {2023},

date = {2023-10-23},

urldate = {2023-10-23},

pages = {510–515},

abstract = {As drones become more widely available, they find applications in many different fields. One of the most promising applications of drones is to use them in deliveries of items/food within certain distances to offer quick service for the customers. In such cases, we will see many different companies deploying drone ducking stations within a neighborhood and fly drones frequently during the day. However, as more companies get into this domain, this will increase the potential for collisions as several drones will simultaneously fly to destinations that are close to each other. Therefore, there is a need to coordinate their trajectory planning in advance by sharing information about their trajectories and destinations. Nevertheless, since drones belong to different companies sharing this information may violate the privacy of their customers and also expose their business privacy. The sharing needs to be done in a privacy},

keywords = {},

pubstate = {published},

tppubtype = {article}

}